Materials Cloud Archive

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<img class="img-max pt-50" src="/static/images/b2find.png"></a> <div class="pt-50"></div> </div> <div class="col-xs-1"></div> </div> </div> <div class="row justify-content-start"> <div class="col col-xs-1"></div> <div class="col-12 col-sm-4 title_frontpage"> <h2>Latest records</h2> </div> <div class="col-12 col-sm-6"> <button type="button" class="btn btn-default float-md-right title_frontpage" onclick="openLogos()"> <img class="max_width_280" src="/static/images/logos.png"> </button> </div> <div class="col col-xs-1"></div> </div> <div class="row"> <div class="col-xs-1"></div> <div class="col-xs-10"> <p class="pl-0 mb-0">Number of published records (all versions): 1134</p> <p class="pl-0 mb-0">Number of published records (latest version): 917</p> <p class="pl-0 mb-0">This page shows the latest version of each record.</p> </div> <div class="col-xs-1"></div> </div> <div class="row pt-30"> <div class="col-xs-1"></div> <div class="col-xs-10"> <div class="row"> <div class="col-xs-12"> <h4 class="record"><a target="_self" href="/record/2024.186"> The dynamic diversity and invariance of ab-initio water</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:mk-fy" class="doi-right">10.24435/materialscloud:mk-fy </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Wei Tian, Chenyu Wang, Ke Zhou </p> <ul class="list-inline"> <li class="text-justify">Comprehending water dynamics is crucial in various fields such as water desalination, ion separation, electrocatalysis, and biochemical processes. While ab-initio molecular dynamics (AIMD) accurately portray water’s structure, computing its dynamic properties over nanosecond timescales proves cost-prohibitive. This study employs machine learning potentials (MLPs) to accurately determine the dynamical properties of liquid water with ab-initio accuracy. Our findings reveal diversity in the calculated diffusion coefficient (D) and viscosity of water (η) across different methodologies. Specifically, while the GGA, meta-GGA, and hybrid functional methods struggle to predict dynamic properties under ambient conditions, whereas methods on the higher level of Jacob’s ladder of DFT approximation perform significantly better. Intriguingly, we discovered that all D and η adhere to the established Stokes-Einstein (SE) relation for all the ab-initio water. The diversity observed among ...</li> </ul> <p> <span> Latest version: v2<br> Publication date: Nov 21, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.185"> Interplay of hydrogen boride sheets with water: An insight into edge stability</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:r5-bt" class="doi-right">10.24435/materialscloud:r5-bt </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Kurt Irvin Rojas, Yoshitada Morikawa, Ikutaro Hamada </p> <ul class="list-inline"> <li class="text-justify">Hydrogen boride (HB) sheet is a newly developed two-dimensional boron-based nanosheet. To explore its potential in electronic and catalytic applications, it's crucial to assess its chemical stability with common substances. In this study, we examine its stability in water, a substance frequently encountered in ambient conditions and various applications. Focusing on the HB sheet's edge, we utilized a nanoribbon model to observe water-edge interactions. This record includes the crystal structures optimized through first-principles calculations, which were used to analyze the properties and interactions between water and the HB sheet. Additionally, input files for system calculations are provided to facilitate reproducibility of the study's results.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Nov 20, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.184"> The effects of size and strain on the electronic behavior of AlN/GaN heterojunction</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:32-q6" class="doi-right">10.24435/materialscloud:32-q6 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Qianjin Lei </p> <ul class="list-inline"> <li class="text-justify">The electronic behavior of AlN/GaN heterojunction is investigated at thicknesses of 1.2~4.2 nm and biaxial strain of -8%~8% based on density functional theory. The structural stability, charge distribution, electronic properties, and transport properties of the cells reveal the enhancing effects of size and strain. With the thickness decreases, the bandgap values of the cells, as low as -0.387 eV, confirm the transition in structural nature, while the individual atomic layers remain bandgaps of at least 0.3 eV in DOS. The carrier mobility enhances to 2×10<sup>12</sup> m<sup>2</sup>/V/s. The cell with 1.2 nm thickness is weakened by 50% due to the quantum confinement effect. Under compressive strain ranging from -8% to 0%, the characteristics of heterojunctions are enhanced by an order of magnitude, in contrast, under 0%~8% tensile strain, the cells decrease slightly but for 2L cells.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Nov 20, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.182"> Predicting electronic screening for fast Koopmans spectral functional calculations</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:w1-ev" class="doi-right">10.24435/materialscloud:w1-ev </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Yannick Schubert, Sandra Luber, Nicola Marzari, Edward Linscott </p> <ul class="list-inline"> <li class="text-justify">Koopmans spectral functionals are a powerful extension of Kohn-Sham density-functional theory (DFT) that enable the prediction of spectral properties with state-of-the-art accuracy. The success of these functionals relies on capturing the effects of electronic screening through scalar, orbital-dependent parameters. These parameters have to be computed for every calculation, making Koopmans spectral functionals more expensive than their DFT counterparts. In this work, we present a machine-learning model that — with minimal training — can predict these screening parameters directly from orbital densities calculated at the DFT level. We show on two prototypical use cases that using the screening parameters predicted by this model, instead of those calculated from linear response, leads to orbital energies that differ by less than 20 meV on average. Since this approach dramatically reduces run-times with minimal loss of accuracy, it will enable the application of Koopmans spectral ...</li> </ul> <p> <span> Latest version: v2<br> Publication date: Nov 14, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.181"> Atomic-scale understanding of oxide growth and dissolution kinetics of Ni-Cr alloys</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:w1-mx" class="doi-right">10.24435/materialscloud:w1-mx </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Penghao Xiao, Brandon Wood </p> <ul class="list-inline"> <li class="text-justify">Passivation oxide formation is the key for corrosion control of metal alloys. The kinetics of competing oxide formation and dissolution determines alloy corrosion behaviors in aqueous solution. Despite the important role of the multi-component oxide evolution, little has been known on the kinetics from the atomistic level. We have built a computational framework that enables simulations of competing kinetic processes in multi-component oxides from first principles. The effects of applied voltage, pH and temperature on oxide growth, dissolution and reprecipitation can all be captured in this model. Combining with our experimental measurements on Alloy 22 and a Ni80%-Cr20% model alloy, we identified three voltage regimes with distinct oxide thicknesses and compositions. The oxide energetics of various stoichiometries are calculated by the density functional theory (DFT). Then the obtained data are used to train a surrogate lattice Hamiltonian with the cluster expansion (CE) ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Nov 14, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.180"> Non-relativistic ferromagnetotriakontadipolar order and spin splitting in hematite</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:w9-qz" class="doi-right">10.24435/materialscloud:w9-qz </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Xanthe Henderike Verbeek, David Voderholzer, Stefan Schären, Yannick Gachnang, Nicola Ann Spaldin, Sayantika Bhowal </p> <ul class="list-inline"> <li class="text-justify">We show that hematite, 𝛼-Fe₂O₃, below its Morin transition, has a ferroic ordering of rank-5 magnetic triakontadipoles on the Fe ions. In the absence of spin-orbit coupling, these are the lowest-order ferroically aligned magnetic multipoles, and they give rise to the g-wave non-relativistic spin splitting in hematite. We find that the ferroically ordered magnetic triakontadipoles result from the simultaneous antiferroic ordering of the charge hexadecapoles and the magnetic dipoles, providing a route to manipulating the magnitude and the sign of the magnetic triakontadipoles as well as the spin splitting. Furthermore, we find that both the ferroic ordering of the magnetic triakontadipoles and many of the spin-split features persist in the weak ferromagnetic phase above the Morin transition temperature.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Nov 07, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.179"> Atomistic simulations of the crystallization of amorphous GeTe nanoparticles</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:xv-jk" class="doi-right">10.24435/materialscloud:xv-jk </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Debdipto Acharya, Omar Abou El Kheir, Simone Perego, Davide Campi, Marco Bernasconi </p> <ul class="list-inline"> <li class="text-justify">The effect of dimensionality reduction on the crystallization kinetics of phase change materials is of relevance for the operation of ultrascaled memory devices. Therefore, the crystallization of amorphous nanoparticles (NPs) of the prototypical phase change compounds, GeTe and Ge₂Sb₂Te₅, has been addressed by several experimental works in recent years. In this work, we performed molecular dynamics simulations of the crystallization process of amorphous GeTe NPs with diameter in the range 3-6 nm (512-4096 atoms) by exploiting a machine-learned interatomic potential. We saw a few crystal nucleation events in the larger NPs but no crystallization in the smallest NP, 3 nm in diameter, in simulations lasting up to 80 ns in the temperature range 500-750 K. The analysis of the crystallization kinetics suggests that the nucleation rate per volume decreases with the NP size to an extent that prevents us from seeing crystallization in the smallest NP on our simulation time scale. This ...</li> </ul> <p> <span> Latest version: v2<br> Publication date: Nov 07, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.178"> Exploring different search approaches to discover donor molecules for organic solar cells</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:t7-5a" class="doi-right">10.24435/materialscloud:t7-5a </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Mohammed Azzouzi, Steven Benett, Victor Posligua, Roberto Bondesan, Martijn Zwijnenburg, Kim Jelfs </p> <ul class="list-inline"> <li class="text-justify">Identifying organic molecules with desirable properties from the extensive chemical space can be challenging, particularly when property evaluation methods are time-consuming and resource intensive. In this study, we illustrate this challenge by exploring the chemical space of large oligomers, constructed from monomeric building blocks, for potential use in organic photovoltaics (OPV). To facilitate this exploration, we developed a Python package called stk-search, which employs a building block approach. For this purpose, we developed a python package to search the chemical space using a building block approach: stk-search. We use stk-search (GitHub link) to compare a variety of search algorithms, including those based upon Bayesian optimization and evolutionary approaches. Initially, we evaluated and compared the performance of different search algorithms within a precomputed search space. We then extended our investigation to the vast chemical space of molecules formed of 6 ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Nov 04, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.177"> Incorporating static intersite correlation effects in vanadium dioxide through DFT+V</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:2k-pr" class="doi-right">10.24435/materialscloud:2k-pr </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Lea Haas, Peter Mlkvik, Nicola A. Spaldin, Claude Ederer </p> <ul class="list-inline"> <li class="text-justify">We analyze the effects on the structural and electronic properties of vanadium dioxide (VO₂) of adding an empirical inter-atomic potential within the density-functional theory+V (DFT+V) framework. We use the DFT+V machinery founded on the extended Hubbard model to apply an empirical self-energy correction between nearest-neighbor vanadium atoms in both rutile and monoclinic phases, and for a set of structures interpolating between these two cases. We observe that imposing an explicit intersite interaction V along the vanadium-vanadium chains enhances the characteristic bonding-antibonding splitting of the relevant bands in the monoclinic phase, thus favoring electronic dimerization and the formation of a band gap. We then explore the effect of V on the structural properties and the relative energies of the two phases, finding an insulating global energy minimum for the monoclinic phase, consistent with experimental observations. With increasing V, this minimum becomes deeper ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 31, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.176"> Single-site DFT+DMFT for vanadium dioxide using bond-centered orbitals</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:cv-jh" class="doi-right">10.24435/materialscloud:cv-jh </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Peter Mlkvik, Maximilian E. Merkel, Nicola A. Spaldin, Claude Ederer </p> <ul class="list-inline"> <li class="text-justify">We present a combined density-functional theory and single-site dynamical mean-field theory (DMFT) study of vanadium dioxide (VO₂) using an unconventional set of bond-centered orbitals as the basis of the correlated subspace. VO₂ is a prototypical material undergoing a metal-insulator transition (MIT), hosting both intriguing physical phenomena and the potential for industrial applications. With our choice of correlated subspace basis, we investigate the interplay of structural dimerization and electronic correlations in VO₂ in a computationally cheaper way compared to other state-of-the-art methods, such as cluster DMFT. Our approach allows us to treat the rutile and M1 monoclinic VO₂ phases on an equal footing and to vary the dimerizing distortion continuously, exploring the energetics of the transition between the two phases. The choice of basis presented in this work hence offers a complementary view on the long-standing discussion of the MIT in VO₂ and suggests possible ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 31, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.175"> Probing the effects of broken symmetries in machine learning</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:kz-3b" class="doi-right">10.24435/materialscloud:kz-3b </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Marcel F. Langer, Sergey N. Pozdnyakov, Michele Ceriotti </p> <ul class="list-inline"> <li class="text-justify">Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to the physical sciences. This is especially true for models targeting the properties of matter at the atomic scale. Both established and state-of-the-art approaches, with almost no exceptions, are built to be exactly equivariant to translations, permutations, and rotations of the atoms. Incorporating symmetries—rotations in particular—constrains the model design space and implies more complicated architectures that are often also computationally demanding. There are indications that unconstrained models can easily learn symmetries from data, and that doing so can even be beneficial for the accuracy of the model. We demonstrate that an unconstrained architecture can be trained to achieve a high degree of rotational invariance, testing the impacts of the small symmetry breaking in realistic scenarios involving ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 30, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.174"> Engineering epitaxial interfaces for topological insulator – superconductor hybrid devices with Al electrodes</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:w3-3c" class="doi-right">10.24435/materialscloud:w3-3c </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Abdur Rehman Jalil, Tobias W. Schmitt, Philipp Rüßmann, Xian-Kui Wei, Benedikt Frohn, Michael Schleenvoigt, Wilhelm Wittl, Xiao Hou, Anne Schmidt, Kaycee Underwood, Gustav Bihlmayer, Martina Luysberg, Joachim Mayer, Stefan Blügel, Detlev Grützmacher, Peter Schüffelgen </p> <ul class="list-inline"> <li class="text-justify">Proximity-induced superconductivity in hybrid devices of topological insulators and superconductors offers a promising platform for the pursuit of elusive topological superconductivity and its anticipated applications, such as fault-tolerant quantum computing. To study and harness such hybrid devices, a key challenge is the realization of highly functional material interfaces with a suitable superconductor featuring 2e-periodic parity-conserving transport to ensure a superconducting hard-gap free of unpaired electrons, which is important for Majorana physics. A superconductor well-known for this characteristic is Al, however, its direct integration into devices based on tetradymite topological insulators has so far been found to yield non-transparent interfaces. By focusing on Bi₂Te₃-Al heterostructures, this study identifies detrimental interdiffusion processes at the interface through atomically resolved structural and chemical analysis, and showcase their mitigation by ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 28, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.173"> Adaptive energy reference for machine-learning models of the electronic density of states</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:zp-ez" class="doi-right">10.24435/materialscloud:zp-ez </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Wei Bin How, Sanggyu Chong, Federico Grasselli, Kevin K. Huguenin-Dumittan, Michele Ceriotti </p> <ul class="list-inline"> <li class="text-justify">The electronic density of states (DOS) provides information regarding the distribution of electronic states in a material, and can be used to approximate its optical and electronic properties and therefore guide computational material design. Given its usefulness and relative simplicity, it has been one of the first electronic properties used as target for machine-learning approaches going beyond interatomic potentials. A subtle but important point, well-appreciated in the condensed matter community but usually overlooked in the construction of data-driven models, is that for bulk configurations the absolute energy reference of single-particle energy levels is ill-defined. Only energy differences matter, and quantities derived from the DOS are typically independent on the absolute alignment. We introduce an adaptive scheme that optimizes the energy reference of each structure as part of training, and show that it consistently improves the quality of ML models compared to ...</li> </ul> <p> <span> Latest version: v3<br> Publication date: Oct 25, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.171"> Thermal conductivity predictions with foundation atomistic models</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:2d-4b" class="doi-right">10.24435/materialscloud:2d-4b </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Balázs Póta, Paramvir Ahlawat, Gábor Csányi, Michele Simoncelli </p> <ul class="list-inline"> <li class="text-justify">Advances in machine learning have led to the development of foundation models for atomistic materials chemistry, enabling quantum-accurate descriptions of interatomic forces across diverse compounds at reduced computational cost. Hitherto, these models have been benchmarked relying on descriptors based on atoms' interaction energies or harmonic vibrations; their accuracy and efficiency in predicting observable and technologically relevant heat-conduction properties remains unknown. Here, we introduce a framework that leverages foundation models and the Wigner formulation of heat transport to overcome the major bottlenecks of current methods for designing heat-management materials: high cost, limited transferability, or lack of physics awareness. We present the standards needed to achieve first-principles accuracy in conductivity predictions through model's fine-tuning, discussing benchmark metrics and precision/cost trade-offs. We apply our framework to a database of solids with ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 22, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.170"> Inter-layer hydrogen recombination from hydrogen boride nanosheets elucidated by isotope labeling</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:y6-p8" class="doi-right">10.24435/materialscloud:y6-p8 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Rojas Kurt Irvin, Shin-ichi Ito, Yukihiro Yasuda, Natsumi Noguchi, Kosei Fukuda, Miwa Hikichi, Zhihao Kang, Mei Yuan, Ryuki Tsuji, Osamu Oki, Susmita Roy, Yasuyuki Hikita, Iwao Matsuda, Masahiro Miyauchi, Ikutaro Hamada, Takahiro Kondo </p> <ul class="list-inline"> <li class="text-justify">Deuterium boride (DB) nanosheets were synthesized through ion exchange, and their Fourier-transform infrared absorption spectra showed isotope effects with a shift in the B-H stretching mode. Temperature-programmed desorption (TPD) experiments indicated that hydrogen release from DB and HB nanosheets primarily results from inter-layer hydrogen recombination at lower temperatures, with intra-layer recombination occurring at higher temperatures. This repository contains supplementary data related to the computational findings presented in the accompanying publication. The goal of sharing these data files is to enhance result transparency and support better reproducibility.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 21, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.169"> Zero-point renormalization of the bandgap, mass enhancement, and spectral functions: Validation of methods and verification of first-principles codes</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:xr-ce" class="doi-right">10.24435/materialscloud:xr-ce </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Samuel Poncé, Jae-Mo Lihm, Cheol-Hwan Park </p> <ul class="list-inline"> <li class="text-justify">Verification and validation of methods and first-principles software are at the core of computational solid-state physics but are too rarely addressed. We compare four first-principles codes: Abinit, Quantum ESPRESSO, EPW, ZG, and three methods: (i) the Allen-Heine-Cardona theory using density functional perturbation theory (DFPT), (ii) the Allen-Heine-Cardona theory using Wannier function perturbation theory (WFPT), and (iii) an adiabatic non-perturbative frozen-phonon method. For these cases, we compute the real and imaginary parts of the electron-phonon self-energy in diamond and BAs, including dipoles and quadrupoles when interpolating. We find excellent agreement between software that implements the same formalism as well as good agreement between the DFPT and WFPT methods. Importantly, we find that the Deybe-Waller term is momentum dependent which impacts the mass enhancement, yielding approximate results when using the Luttinger approximations. Finally, we compare the ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 21, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.168"> Isotope-dependent site occupation of hydrogen in epitaxial titanium hydride nanofilms</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:je-ev" class="doi-right">10.24435/materialscloud:je-ev </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Takahiro Ozawa, Yuki Sugisawa, Yuya Komatsu, Ryota Shimizu, Taro Hitosugi, Daiichiro Sekiba, Kunihiko Yamauchi, Ikutaro Hamada, Katsuyuki Fukutani </p> <ul class="list-inline"> <li class="text-justify">Identification of the hydrogen lattice location in crystals is key to understanding and controlling hydrogen-induced properties. Combining nuclear reaction analysis with the ion channeling technique, we experimentally determined the locations of H and D in epitaxial nanofilms of titanium hydrides. It was found that 11 at.% of H are located at the octahedral site with the remaining H atoms in the tetrahedral site. Density functional theory calculations revealed that the structures with the partial octahedral site occupation are stabilized by the Fermi level shift and Jahn-Teller effect induced by hydrogen. In contrast, D was found to solely occupy the tetrahedral site owing to the mass effect on the zero-point vibrational energy. These findings suggest that site occupation of hydrogen can be controlled by changing the isotope mixture ratio, which leads to promising manifestation of novel hydrogen-related phenomena.</li> </ul> <p> <span> Latest version: v2<br> Publication date: Oct 17, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.166"> A prediction rigidity formalism for low-cost uncertainties in trained neural networks</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:5r-rf" class="doi-right">10.24435/materialscloud:5r-rf </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Filippo Bigi, Sanggyu Chong, Michele Ceriotti, Federico Grasselli </p> <ul class="list-inline"> <li class="text-justify">Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based on the solution of a constrained optimization problem, this work proposes ‘prediction rigidities’ as a formalism to obtain uncertainties of arbitrary pre-trained regressors. A clear connection between the suggested framework and Bayesian inference is established, and a last-layer approximation is developed and rigorously justified to enable the application of the method to neural networks. This extension affords cheap uncertainties without any modification to the neural network itself or its training procedure. The effectiveness of this approach is shown for a wide range of regression tasks, ranging from simple toy models to applications in chemistry and meteorology. This record includes computational experiments supporting the MLST paper titled "A prediction rigidity formalism for low-cost uncertainties in trained neural networks".</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 17, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.165"> Glassy dynamics and crystalline local order in two-dimensional amorphous silica</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:5b-bc" class="doi-right">10.24435/materialscloud:5b-bc </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Marco Dirindin, Daniele Coslovich </p> <ul class="list-inline"> <li class="text-justify">We reassess the modeling of amorphous silica bilayers as a two-dimensional classical system whose particles interact with an effective pairwise potential. We show that it is possible to reparameterize the potential developed by Roy, Heyde, and Heuer to quantitatively match the structural details of the experimental samples. We then study the glassy dynamics of the reparameterized model at low temperatures. Using appropriate cage-relative correlation functions, which suppress the effect of Mermin-Wagner fluctuations, we highlight the presence of two well-defined Arrhenius regimes separated by a narrow crossover region, which we connect to the thermodynamic anomalies and the changes in the local structure. We find that the bond-orientational order grows steadily below the crossover temperature and is associated to transient crystalline domains of nanometric size. These findings raise fundamental questions about the nature of glass structure in two dimensions and provide guidelines ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 16, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.164"> DFT calculations of surface binding and interstitial hydrogen formation energies for plasma-facing materials</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:9k-09" class="doi-right">10.24435/materialscloud:9k-09 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Andrea Fedrigucci, Nicola Marzari, Paolo Ricci </p> <ul class="list-inline"> <li class="text-justify">This dataset contains the results of density functional theory (DFT) calculations performed using Quantum ESPRESSO to study surface binding energies (SBE) and the formation energies of interstitial hydrogen (H-IFE) in various plasma-facing materials (PFMs). These calculations support the findings reported in the article Comprehensive Screening of Plasma-Facing Materials for Nuclear Fusion, where a combination of peer-reviewed data from the PAULING FILE database and first-principles calculations are used to evaluate potential PFM candidates. Key results include a detailed comparison of tungsten and alternative refractory materials, focusing on their behavior under intense neutron bombardment and plasma interactions in nuclear fusion reactors. The dataset includes input and output files from the Quantum ESPRESSO simulations, offering valuable insight into defect energetics in candidate materials.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 16, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.163"> Benchmarking machine-readable vectors of chemical reactions on computed activation barriers</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:xd-10" class="doi-right">10.24435/materialscloud:xd-10 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Puck van Gerwen, Ksenia R. Briling, Yannick Calvino Alonso, Malte Franke, Clemence Corminboeuf </p> <ul class="list-inline"> <li class="text-justify">In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging from graph-based models to methods based on the three-dimensional structure of reactants and products. In tandem, many representations have been developed to predict experimental targets, which may hold promise for barrier prediction as well. Here, we bring together all of these efforts and benchmark various methods (Morgan fingerprints, the DRFP, the CGR representation-based Chemprop, SLATM<sub>d</sub>, B²R<sub>l</sub>², EquiReact and language model BERT + RXNFP) for the prediction of computed activation barriers on three diverse datasets. This record includes data to support the article "Benchmarking machine-readable vectors of chemical reactions on computed activation barriers". This supports the github repository ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 16, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.162"> Density functional theory study of silicon nanowires functionalized by grafting organic molecules</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:ps-nw" class="doi-right">10.24435/materialscloud:ps-nw </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Sara Marchio, Francesco Buonocore, Simone Giusepponi, Massimo Celino </p> <ul class="list-inline"> <li class="text-justify">Functionalizing Silicon Nanowires (SiNWs) through covalent attachment of organic molecules offers diverse advantages, including surface passivation, introduction of new functionalities, and enhanced material performance in applications like electronic devices and biosensors. Given the wide range of available functional molecules, systematic large-scale screening is crucial. Therefore, we developed an automated computational workflow using Python scripts in conjunction with the AiiDa framework to explore structural configurations of functional molecules adsorbed onto silicon surfaces. This workflow generates multiple adhesion configurations corresponding to different binding orientations using surface and functional molecule structures as inputs. This dataset contains data related to the structural optimization of molecules with single, double, and triple carbon-carbon bonds attached to the nanowire surface in various adhesion configurations. We describe the chemisorption on ...</li> </ul> <p> <span> Latest version: v2<br> Publication date: Oct 15, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.161"> 3DReact: geometric deep learning for chemical reactions</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:xd-ef" class="doi-right">10.24435/materialscloud:xd-ef </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Puck van Gerwen, Ksenia Briling, Charlotte Bunne, Vignesh Ram Somnath, Ruben Laplaza, Andreas Krause, Clemence Corminboeuf </p> <ul class="list-inline"> <li class="text-justify">Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we introduce 3DREACT, a geometric deep learning model to predict reaction properties from three-dimensional structures of reactants and products. We demonstrate that the invariant version of the model is sufficient for existing reaction data sets. We illustrate its competitive performance on the prediction of activation barriers on the GDB7-22-TS, Cyclo-23-TS, and Proparg-21-TS data sets in different atom-mapping regimes. We show that, compared to existing models for reaction property prediction, 3DREACT offers a flexible framework that exploits atom- mapping information, if available, as well as geometries of reactants and products (in an invariant or equivariant fashion). Accordingly, it performs systematically well across different ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 15, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.160"> Dataset of self-consistent Hubbard parameters for Ni, Mn and Fe from linear-response</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:r5-42" class="doi-right">10.24435/materialscloud:r5-42 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Martin Uhrin, Austin Zadoks, Luca Binci, Nicola Marzari, Iurii Timrov </p> <ul class="list-inline"> <li class="text-justify">Density-functional theory with extended Hubbard functionals (DFT+U+V) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled d and f electronic states. However, achieving accuracy in this approach hinges upon the accurate determination of the on-site U and inter-site V Hubbard parameters. In practice, these are obtained either by semi-empirical tuning, requiring prior knowledge, or, more correctly, by using predictive but expensive first-principles calculations. This archive entry contains Hubbard parameters, occupation matrices and other data calculated for 28 materials and covers all steps in a self-consistent procedure where, at each step new Hubbard parameters are obtained via linear-response, a process that is repeated until the parameters no longer change. The ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 15, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.159"> Homogeneous nucleation of undercooled Al-Ni melts via a machine-learned interaction potential</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:dq-ax" class="doi-right">10.24435/materialscloud:dq-ax </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Johannes Sandberg, Thomas Voigtmann, Emilie Devijver, Noel Jakse </p> <ul class="list-inline"> <li class="text-justify">Homogeneous nucleation processes are important for understanding solidification and the resulting microstructure of materials. Simulating this process requires accurately describing the interactions between atoms, hich is further complicated by chemical order through cross-species interactions. The large scales needed to observe rare nucleation events are far beyond the capabilities of ab initio simulations. Machine-learning is used for overcoming these limitations in terms of both accuracy and speed, by building a high-dimensional neural network potential for binary Al-Ni alloys, which serve as a model system relevant to many industrial applications. The potential is validated against experimental diffusion, viscosity, and scattering data, and is applied to large-scale molecular dynamics simulations of homogeneous nucleation at equiatomic composition, as well as for pure Ni. Pure Ni nucleates in a single-step into an fcc crystal phase, in contrast to previous results obtained ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 11, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.157"> Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds</a> <span class="interfaces discover"><a href="https://www.materialscloud.org/discover/2dstructures"></a></span> <span class="interfaces explore"><a href="https://www.materialscloud.org/explore/mc2d"></a></span> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:yf-kf" class="doi-right">10.24435/materialscloud:yf-kf </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Nicolas Mounet, Marco Gibertini, Philippe Schwaller, Davide Campi, Andrius Merkys, Antimo Marrazzo, Thibault Sohier, Ivano E. Castelli, Andrea Cepellotti, Giovanni Pizzi, Nicola Marzari </p> <ul class="list-inline"> <li class="text-justify">Two-dimensional (2D) materials have emerged as promising candidates for next-generation electronic and optoelectronic applications. Yet, only a few dozens of 2D materials have been successfully synthesized or exfoliated. Here, we search for novel 2D materials that can be easily exfoliated from their parent compounds. Starting from 108423 unique, experimentally known three-dimensional compounds we identify a subset of 5619 that appear layered according to robust geometric and bonding criteria. High-throughput calculations using van-der-Waals density-functional theory, validated against experimental structural data and calculated random-phase-approximation binding energies, allow to identify 1825 compounds that are either easily or potentially exfoliable. In particular, the subset of 1036 easily exfoliable cases provides novel structural prototypes and simple ternary compounds as well as a large portfolio of materials to search from for optimal properties. For a subset of 258 ...</li> </ul> <p> <span> Latest version: v5<br> Publication date: Oct 11, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.156"> Quantum simulations of radiation damage in a molecular polyethylene analog</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:es-25" class="doi-right">10.24435/materialscloud:es-25 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Nathaniel Troup, Matthew P Kroonblawd, Davide Donadio, Nir Goldman </p> <ul class="list-inline"> <li class="text-justify">An atomic-level understanding of radiation-induced damage in simple polymers like polyethylene is essential for determining how these chemical changes can alter the physical and mechanical properties of important technological materials such as plastics. We performed ensembles of quantum simulations of radiation damage in a polyethylene analog using the Density Functional Tight Binding method to help bind its radiolysis and subsequent degradation as a function of radiation dose. Chemical degradation products are categorized with a graph theory approach, and we compute occurrence rates of unsaturated carbon bond formation, crosslinking, cycle formation, chain scission reactions, and out-gassing products. Statistical correlations between product pairs show significant correlations between chain scission reactions, unsaturated carbon bond formation, and out-gassing products, though these correlations decrease with increasing atom recoil energy. Our results present relatively simple ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 11, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.155"> A deep learning dataset for metal multiaxial fatigue life prediction</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:2d-bz" class="doi-right">10.24435/materialscloud:2d-bz </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Shuonan Chen, Yongtao Bai*, Xuhong Zhou*, Ao Yang </p> <ul class="list-inline"> <li class="text-justify">In this work, we present a comprehensive dataset designed to facilitate the prediction of metal fatigue life using deep learning techniques. The dataset includes detailed experimental data from 40 different metallic materials, comprising a total of 1195 data points under 48 distinct loading paths. Each data point is stored in a CSV file, capturing the loading path as a time-series with axial and tangential stress or strain values.The primary purpose of this dataset is to support the development and validation of deep learning models aimed at accurately predicting the fatigue life of metals under various loading conditions. This dataset includes stress-controlled and strain-controlled data, ensuring a broad representation of experimental scenarios. Additionally, an Excel file accompanies the dataset, providing detailed mechanical properties of each material, such as elastic modulus, tensile strength, yield strength, and Poisson's ratio, along with references to the original ...</li> </ul> <p> <span> Latest version: v3<br> Publication date: Oct 10, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.154"> High-throughput screening of 2D materials identifies p-type monolayer WS2 as potential ultra-high mobility semiconductor</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:aw-d3" class="doi-right">10.24435/materialscloud:aw-d3 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Viet-Anh Ha, Feliciano Giustino </p> <ul class="list-inline"> <li class="text-justify">2D semiconductors are considered as a promising alternative to silicon for future electronics. This class of materials possesses different advantages including atomically sharp surfaces and the ability to scale channel thickness down to a single layer. However, they typically exhibit lower charge carrier mobility as well as higher contact resistance compared to 3D semiconductors, which deters the development of high-performance devices at scale. In this work, we searched for high-mobility 2D materials by combining high-throughput screening approach and advanced transport calculations based on the ab initio Boltzmann transport equation. Based on our calculations, we identified several promising candidates channel materials, and in particular monolayer WS₂ which exhibits a phonon-limited hole mobility in excess of 1300 cm²/Vs. Our work suggests that WS₂ can be ideal for channel of high-performance 2D transistors with Ohmic contacts and low defect density. This work has been published in [npj Comput. Mater. 10, 229 (2024)].</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 10, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.153"> Dataset of tensile properties for sub-sized specimens of nuclear structural materials</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:yf-3j" class="doi-right">10.24435/materialscloud:yf-3j </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Longze Li, John Merickel, Yalei Tang, Rongjie Song, Joshua Rittenhouse, Aleksandar Vakanski, Fei Xu </p> <ul class="list-inline"> <li class="text-justify">The dataset provides records of tensile properties of nuclear structural materials. The focus is on studying the influence of specimen dimensions and geometry on mechanical properties such as yield strength, ultimate tensile strength, uniform elongation, and total elongation. The dataset was created through an extensive literature review of scientific articles and databases. The search inclusion criteria targeted peer-reviewed studies on tensile testing of sub-sized specimens, providing quantitative data on tensile properties relative to specimen size. The extracted data points from the literature review were organized into a tabular format database containing 1,070 tensile testing records with 54 parameters, including material type and composition, manufacturing information, irradiation conditions, specimen size and dimensions, and tensile properties. Materials science experts conducted systematic checks to validate the collected data, ensuring accuracy in the material type, ...</li> </ul> <p> <span> Latest version: v2<br> Publication date: Oct 10, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.152"> Simulated 3d transition metal oxides cation K-edge XANES dataset</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:85-8x" class="doi-right">10.24435/materialscloud:85-8x </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Shubha R. Kharel, Fanchen Meng, Xiaohui Qu, Matthew R. Carbone, Deyu Lu </p> <ul class="list-inline"> <li class="text-justify">X-ray absorption spectroscopy (XAS) is a powerful characterization technique for probing the local chemical environment of absorbing atoms. However, analyzing XAS data presents with significant challenges, often requiring extensive, computationally intensive simulations, as well as significant domain expertise. These limitations hinder the development of fast, robust XAS analysis pipelines that are essential in high-throughput studies and for autonomous experimentation. We address these challenges with a suite of transfer learning approaches for XAS prediction, each uniquely contributing to improved accuracy and efficiency, as demonstrated on simulated K-edge X-ray absorption near-edge structure (XANES) spectra database covering eight 3d transition metals (Ti-Cu). This database contains FEFF and VASP K-edge XANES spectra of 3d transition metal-containing oxide materials used to develop the above machine learning models. The materials structures are sourced from the Materials ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 09, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.151"> Revising known concepts for novel applications: Fe incorporation into Ni-MOF-74 derived oxygen evolution electrocatalysts for anion exchange membrane water electrolysis</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:q2-rd" class="doi-right">10.24435/materialscloud:q2-rd </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Julia Linke, Thomas Rohrbach, Adam Hugh Clark, Camelia Borca, Thomas Huthwelker, Fabian Luca Buchauer, Mikkel Rykær Kraglund, Christodoulos Chatzichristodoulou, Eibhlin Meade, Julie Guehl, Mateusz Wojtas, Marco Ranocchiari, Thomas Justus Schmidt, Emiliana Fabbri </p> <ul class="list-inline"> <li class="text-justify">The performance of Ni-based oxygen evolution reaction (OER) electrocatalysts is enhanced upon Fe incorporation into the structure or Fe uptake from the electrolyte. In light of the promising potential of metal-organic framework (MOF) electrocatalysts for water splitting, Ni-MOF-74 is used as a model catalyst to study the effect of Fe incorporation from KOH electrolyte on the electrocatalyst’s OER activity and stability. The insights obtained from X-ray diffraction and operando X-ray absorption spectroscopy characterizations of Ni-MOF-74 and an amorphous Ni metal organic compound (Ni-MOC*) reveal that Fe uptake enhances OER by two processes: higher Ni oxidation states and enhanced flexibility of the electronic state and local structure when cycling the potential below and above the OER onset. To demonstrate the impressive OER activity and stability in Fe containing KOH, an Ni-MOC* anode was implemented in an anion exchange membrane water electrolyzer (AEM-WE) with 3 ppm Fe ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 09, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.150"> Dislocation-grain boundary interaction dataset for FCC Cu</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:66-am" class="doi-right">10.24435/materialscloud:66-am </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Khanh Dang, Sumit Suresh, Avanish Mishra, Ian Chesser, Nithin Matthew, Edward Kober, Saryu Fensin </p> <ul class="list-inline"> <li class="text-justify">Dislocation-grain boundary play a major role in the strength and ductility of structural materials. An understanding of governing parameters such as grain boundary local atomic structures on the outcome of this interaction can accelerate new alloy design strategies that tailors materials’ microstructures. Previous studies have focused only on the subset of minimum-energy grain boundary structures. We present a comprehensive database of dislocation-grain boundary interaction for edge, screw, and 60o mixed dislocation with 449 < 110 > and < 112 > symmetric tilt grain boundary in FCC Cu consisting of 67 minimum-energy grain boundary structures and 389 metastable ones. The dataset contains the outcomes for 5593 unique interactions with a particular dislocation type, grain boundary structure, and applied shear stress.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 08, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.149"> Influence of carrier-carrier interactions on the sub-threshold swing of band-to-band tunnelling transistors</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:sm-90" class="doi-right">10.24435/materialscloud:sm-90 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Chen Hao Xia, Leonard Deuschle, Jiang Cao, Alexander Maeder, Mathieu Luisier </p> <ul class="list-inline"> <li class="text-justify">Band-to-band tunnelling field-effect transistors (TFETs) have long been considered as promising candidates for future low-power logic applications. However, fabricated TFETs rarely reach sub-60 mV/dec sub-threshold swings (SS) at room temperature. Previous theoretical studies identified Auger processes as possible mechanisms for the observed degradation of SS. Through first-principles quantum transport simulations incorporating carrier-carrier interactions within the Non-equilibrium Green's Function formalism through self-consistent GW approximation, we confirm here that Auger processes are indeed at least partly responsible for the poor performance of TFETs. Using a carbon nanotube TFET as testbed, we show that carrier-carrier scattering alone significantly increases the OFF-state current of these devices, thus worsening their sub-threshold behavior. The results are in the folder uploaded.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 08, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.148"> High-throughput computation of ab initio Raman spectra for two-dimensional materials</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:bq-vp" class="doi-right">10.24435/materialscloud:bq-vp </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Geng Li, Yingxiang Gao, Daiyou Xie, Leilei Zhu, Dongjie Shi, Shuming Zeng, Wei Zhan, Jun Chen, Honghui Shang </p> <ul class="list-inline"> <li class="text-justify">Raman spectra play an important role in characterizing two-dimensional materials, as they provide a direct link between the atomic structure and the spectral features. In this work, we present an automatic computational workflow for Raman spectra using all-electron density functional perturbation theory. Utilizing this workflow, we have successfully completed the Raman spectra calculation for 3504 different two-dimensional materials, with the resultant data saved in a data repository.</li> </ul> <p> <span> Latest version: v2<br> Publication date: Oct 04, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.147"> Disorder-resilient transition of Helical to Conical ground states in M<sub>1/3</sub>NbS<sub>2</sub>, M=Cr,Mn</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:6v-9v" class="doi-right">10.24435/materialscloud:6v-9v </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Manaswini Sahoo, Pietro Bonfà, Amelia Elisabeth Hall, Daniel A. Mayoh, Laura Teresa Corredor, Anja U. B. Wolter, Bernd Büchner, Geetha Balakrishnan, Roberto De Renzi, Giuseppe Allodi </p> <ul class="list-inline"> <li class="text-justify">The discovery of chiral helical magnetism (CHM) in Cr<sub>1/3</sub>NbS<sub>2</sub> and the stabilization of a chiral soliton lattice (CSL) has attracted considerable interest in view of their potential technological applications. However, there is an ongoing debate regarding whether the sister compound, Mn<sub>1/3</sub>NbS<sub>2</sub>, which shares the same crystal structure, exhibits similar nontrivial properties, which rely on the stabilization of the lack of inversion symmetry at the magnetic ion. In this study, we conduct a comprehensive investigation of the magnetically ordered states of both compounds, using <sup>53</sup>Cr, <sup>55</sup>Mn and <sup>93</sup>Nb nuclear magnetic resonance. Our results, supported by density functional calculations, detect in a high quality single crystal of Cr<sub>1/3</sub>NbS<sub>2</sub> all the signatures of the monoaxial CHM in a magnetic field, identifying it as a textbook NMR case. The detailed understanding of this prototypic behavior ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 03, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.146"> Organic solar cells with eco-friendly preparation to achieve an efficiency of 20%</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:p3-qv" class="doi-right">10.24435/materialscloud:p3-qv </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Rui Zeng, Feng Liu </p> <ul class="list-inline"> <li class="text-justify">Thin film organic photovoltaics (OPVs) aspire to extract solar energy in a green, high efficiency, and cost-effective pathway, offering a sustainable solution to energy and ecosystem. We take the effort of developing low ecological harmful solvent processing of OPV devices and mini-modules, to strengthen the environmental benefits of OPV. New formula utilizing safer solvents enhanced with additives affords an optimal laboratory device efficiency of 20% as well as superior operational and thermal stability. Mini-module shows an efficiency up to 17.6% in upscaled processing, representing the highest performance for green solvent OPV development. Suitable nanoscaled phase separation is obtained in together with a micron-scale surface wrinkle pattern from the new developed processing strategy. Thus efficient photon-to-electron conversion at nano-interfaces and light extraction in broader receiving angles is achieved, which allows for more flexible installation geometries in building-integrated applications.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 02, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.145"> Oxygen vacancy induced defect dipoles in BiVO4 for photoelectrocatalytic partial oxidation of methane</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:gf-jd" class="doi-right">10.24435/materialscloud:gf-jd </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Xianlong Li, Zhiliang Wang*, Alireza Sasani, Ardeshir Baktash, Kai Wang, haijiao Lu, jiakang you, Peng Chen, Ping Chen, Yifan Bao, Shujun Zhang, Gang Liu, Lianzhou Wang* </p> <ul class="list-inline"> <li class="text-justify">A strong driving force for charge separation and transfer in semiconductors is essential for designing effective photoelectrodes for solar energy conversion. While defect engineering and polarization alignment can enhance this process, their potential interference within a photoelectrode remains unclear. Here we show that oxygen vacancies in bismuth vanadate (BiVO4) can create defect dipoles due to a disruption of symmetry. The modified photoelectrodes exhibit a strong correlation between charge separation and transfer capability and external electrical poling, which is not seen in unmodified samples. Applying poling at -150 Volt boosts charge separation and transfer efficiency to over 90 %. A photocurrent density of 6.3 mA cm-2 is achieved on the photoelectrode after loading with a nickel-iron oxide-based cocatalyst. Furthermore, using generated holes for methane partial oxidation can produce methanol with a Faradaic efficiency of approximately 6 %. These findings provide ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Oct 01, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.144"> Experimental and Theoretical Insights on Gas Trapping in MOFs: A Case Study with Noble Gases and MFU-4 Type MOFs</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:6e-aj" class="doi-right">10.24435/materialscloud:6e-aj </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Hana Bunzen, Beliz Sertcan Gökmen, Andreas Kalytta-Mewes, Maciej Grzywa, Jakub Wojciechowski, Jürg Hutter, Anna-Sophia Hehn, Dirk Volkmer </p> <ul class="list-inline"> <li class="text-justify">Isostructural metal-organic frameworks (MOFs), namely MFU-4 and MFU-4-Br, in which the pore apertures are defined by anionic side ligands (Cl− and Br−, respectively), were synthesized and loaded with noble gases. By selecting the type of side ligand, one can fine-tune the pore aperture size, allowing for precise regulation of the entry and release of gas guests. In this study, we conducted experiments to examine gas loading and release using krypton and xenon as model gases, and we complemented our findings with computational modeling. Remarkably, the loaded gas guests remained trapped inside the pores even after being exposed to air under ambient conditions for extended periods, in some cases for up to several weeks. Therefore, we focused on determining the energy barrier preventing gas release using both theoretical and experimental methods. The results were compared in relation to the types of hosts and guests, providing valuable insights into the gas trapping process in MOFs, ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Sep 26, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.143"> Density functional Bogoliubov-de Gennes theory for superconductors implemented in the SIESTA code</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:3k-4e" class="doi-right">10.24435/materialscloud:3k-4e </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Riccardo Reho, Nils Wittemeier, Arnold Herman Kole, Pablo Ordejón, Zeila Zanolli </p> <ul class="list-inline"> <li class="text-justify">We present SIESTA-BdG, an implementation of the simultaneous solution of the Bogoliubov-de Gennes (BdG) and Density Functional Theory (DFT) problem in SIESTA, a first-principles method and code for material simulations which uses pseudopotentials and a localized basis set. This unified approach describes both conventional and unconventional superconducting states, and enables a description of inhomogeneous superconductors and heterostructures. We demonstrate the validity, accuracy, and efficiency of SIESTA-BdG by computing physically relevant quantities (superconducting charge density, band structure, superconducting gap features, density of states) for conventional singlet (Nb, Pb) and unconventional (FeSe) superconductors. We find excellent agreement with experiments and results obtained within the KKR-BdG computational framework. SIESTA-BdG forms the basis for modelling quantum transport in superconducting devices and including - in an approximate fashion - the superconducting ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Sep 26, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.142"> FINALES (06/2022) – Electrolyte Optimization for Minimum Density and Maximum Viscosity</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:ph-jb" class="doi-right">10.24435/materialscloud:ph-jb </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Monika Vogler, Jonas Busk, Hamidreza Hajiyani, Peter Bjørn Jørgensen, Nehzat Safaei, Ivano E. Castelli, Francisco Fernando Ramirez, Johan M. Carlsson, Giovanni Pizzi, Simon Clark, Felix Hanke, Arghya Bhowmik, Helge S. Stein </p> <ul class="list-inline"> <li class="text-justify">This study presents the initial implementation of the Fast INtention-Agnostic LEarning Server (FINALES) in a demonstration of a distributed Materials Acceleration Platform (MAP) including experimental and computational methods and a machine learning (ML)-based optimizer. In this demonstration, the optimizer was configured to minimize the density of the electrolyte solutions while maximizing the viscosity by exploiting experimental and computational results. The tenants (the units connected to FINALES in the MAP) are shortly described in the following: - Autonomous Synthesis and Analysis of Battery electrolytes (ASAB) setup: an experimental tenant providing density and viscosity data using a densimeter of the type DMA 4100M and a viscometer of type Lovis 2000 both by Anton Paar Germany - Molecular dynamics tenant: a computational tenant capable of providing radial distribution functions, diffusion coefficients, ionic conductivity, transference numbers, heat capacity and density ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Sep 26, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.141"> High-throughput magnetic co-doping and design of exchange interactions in topological insulators</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:b7-6k" class="doi-right">10.24435/materialscloud:b7-6k </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Rubel Mozumder, Johannes Wasmer, David Antognini Silva, Stefan Blügel, Philipp Rüßmann </p> <ul class="list-inline"> <li class="text-justify">Using high-throughput automation of ab-initio impurity-embedding simulations we created a database of 3d and 4d transition metal defects embedded into the prototypical topological insulators (TIs) Bi₂Te₃ and Bi₂Se₃. We simulate both single impurities as well as impurity dimers at different impurity-impurity distances inside the topological insulator matrix. We extract changes to magnetic moments, analyze the polarizability of non-magnetic impurity atoms via nearby magnetic impurity atoms and calculate the exchange coupling constants for a Heisenberg Hamiltonian. We uncover chemical trends in the exchange coupling constants and discuss the impurities' potential with respect to magnetic order in the fields of quantum anomalous Hall insulators. In particular, we predict that co-doping of different magnetic dopants is a viable strategy to engineer the magnetic ground state in magnetic TIs.</li> </ul> <p> <span> Latest version: v2<br> Publication date: Sep 23, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.140"> Excited-state forces with the Gaussian and augmented plane wave method for the Tamm–Dancoff approximation of time-dependent density functional theory</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:92-b6" class="doi-right">10.24435/materialscloud:92-b6 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Beliz Sertcan Gökmen, Jürg Hutter, Anna-Sophia Hehn </p> <ul class="list-inline"> <li class="text-justify">Augmented plane wave methods enable an efficient description of atom-centered or localized features of the electronic density, circumventing high energy cutoffs and thus prohibitive computational costs of pure plane wave formulations. To complement existing implementations for ground-state properties and excitation energies, we present the extension of the Gaussian and augmented plane wave method to excited-state nuclear gradients within the Tamm–Dancoff approximation of time-dependent density functional theory and its implementation in the CP2K program package. Benchmarks for a test set of 35 small molecules demonstrate that maximum errors in the nuclear forces for excited states of singlet and triplet spin multiplicity are smaller than 0.1 eV/Å. The method is furthermore applied to the calculation of the zero-phonon line of defective hexagonal boron nitride. This spectral feature is reproduced with an error of 0.6 eV in comparison to GW–Bethe–Salpeter reference computations and ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Sep 23, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.139"> High-throughput screening of nano-hybrid metal–organic-frameworks for photocatalytic CO₂ reduction</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:46-31" class="doi-right">10.24435/materialscloud:46-31 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Moin Khwaja, Takuya Harada </p> <ul class="list-inline"> <li class="text-justify">Photocatalytic conversion of CO₂ into fuel feed stocks is a promising method for sustainable fuel production. A highly attractive class of materials, inorganic-core@metal–organic-framework heterogeneous catalysts, boasts a significant increase in catalytic performance when compared to the individual materials. However, due to the ever-expanding chemical space of inorganic-core catalysts and metal–organic frameworks (MOFs), identification of these optimal heterojunctions is difficult without appropriate computational screening. In this work, a novel high-throughput screening method of nano-hybrid photocatalysts is presented by screening 65'784 inorganic-core materials and 20'375 MOF-shells for their ability to reduce CO₂ based on their synthesizability, aqueous stability, visible light absorption, and electronic structure; the passing materials were then paired based on their electronic structure to create novel heterojunctions. The results showed 58 suitable inorganic-core ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Sep 19, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.138"> Quasiparticle self-consistent GW with effective vertex corrections in the polarizability and the self-energy applied to MnO, FeO, CoO, and NiO</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:e2-k7" class="doi-right">10.24435/materialscloud:e2-k7 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Mohamed S. Abdallah, Alfredo Pasquarello </p> <ul class="list-inline"> <li class="text-justify">Through quasiparticle self-consistent GW, we investigate the electronic structure of the antiferromagnetic ground state of four transition-metal monoxides: MnO, FeO, CoO, and NiO. In addition to the random-phase approximation, we consider two different schemes incorporating effective vertex corrections. The first scheme includes in the polarizability a vertex function derived from the solution of the Bethe-Salpeter equation (BSE), whereas the second scheme includes in both the polarizability and self-energy a vertex function, which carries a long-range part satisfying the Ward identity and a short-range part derived from the adiabatic local density approximation. Our results include fundamental band gaps, macroscopic dielectric constants, and local magnetic moments, emphasizing the role of vertex corrections in the description of these key electronic properties. We provide quasiparticle band structures and projected densities of states allowing us to establish a connection with ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Sep 17, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.137"> Guidelines for accurate and efficient calculations of mobilities in two-dimensional semiconductors</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:th-te" class="doi-right">10.24435/materialscloud:th-te </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Jiaqi Zhou, Samuel Poncé, Jean-Christophe Charlier </p> <ul class="list-inline"> <li class="text-justify">Emerging two-dimensional (2D) materials bring unprecedented opportunities for electronic applications. The design of high-performance devices requires an accurate prediction of carrier mobility in 2D materials, which can be obtained using state-of-the-art ab initio calculations. However, various factors impact the computational accuracy, leading to contradictory estimations for the mobility. In this work, targeting accurate and efficient ab initio calculations, transport properties in III-V monolayers are reported using the Boltzmann transport equation, and the influences of pseudopotential, quadrupole correction, Berry connection, and spin-orbit coupling (SOC) on mobilities are systematically investigated. Our findings are as follows: (1) The inclusion of semi-core states in pseudopotentials is important to obtain accurate calculations. (2) The variations induced by dynamical quadrupole and Berry connection when treating long range fields can be respectively 40% and 10%. (3) The ...</li> </ul> <p> <span> Latest version: v2<br> Publication date: Sep 17, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.136"> Crystal structure validation of verinurad via proton-detected ultra-fast MAS NMR and machine learning</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:qk-x9" class="doi-right">10.24435/materialscloud:qk-x9 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Daria Torodii, Jacob Holmes, Pinelopi Moutzouri, Sten Nilsson Lill, Manuel Cordova, Arthur Pinon, Kristof Grohe, Sebastian Wegner, Okky Dwichandra Putra, Stefan Norberg, Anette Welinder, Staffan Schantz, Lyndon Emsley </p> <ul class="list-inline"> <li class="text-justify">The recent development of ultra-fast MAS (>100 kHz) provides new opportunities for structural characterization in solids. Here we use NMR crystallography to validate the structure of verinurad, a microcrystalline active pharmaceutical ingredient. To do this, we take advantage of 1H resolution improvement at ultra-fast MAS and use solely 1H-detected experiments and machine learning methods to assign all the experimental proton and carbon chemical shifts. This framework provides a new tool for elucidating chemical information from crystalline samples with limited sample volume and yields remarkably faster acquisition times compared to 13C-detected experiments, without the need to employ dynamic nuclear polarization.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Sep 17, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.135"> Capturing dichotomic solvent behavior in solute–solvent reactions with neural network potentials</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:fq-k5" class="doi-right">10.24435/materialscloud:fq-k5 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Frédéric Célerse, Veronika Juraskova, Shubhajit Das, Matthew D. Wodrich, Clémence Corminboeuf </p> <ul class="list-inline"> <li class="text-justify">Simulations of chemical reactivity in condensed phase systems represent an ongoing challenge in computational chemistry, where traditional quantum chemical approaches typically struggle with both the size of the system and the potential complexity of the reaction. Here, we introduce a workflow aimed at efficiently training neural network potentials (NNPs) to explore energy barriers in solution at the hybrid density functional theory level. The computational burden associated with training at the PBE0-D3(BJ) level is bypassed through the use of active and transfer learning techniques, whereas extensive sampling of the transition state region is accelerated by well-tempered metadynamics simulations using multiple time-step integration. These NNPs serve to explore a puzzling solute--solvent reactivity route involving the ring opening of N-enoxyphthalimide experimentally observed in methanol but not in 2,2,2-trifluoroethanol (TFE). This reaction represents a challenging example ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Sep 09, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.134"> Enhanced spin Hall ratio in two-dimensional semiconductors</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:g7-pk" class="doi-right">10.24435/materialscloud:g7-pk </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Jiaqi Zhou, Samuel Poncé, Jean-Christophe Charlier </p> <ul class="list-inline"> <li class="text-justify">The conversion efficiency from charge current to spin current via spin Hall effect is evaluated by the spin Hall ratio (SHR). Through state-of-the-art ab initio calculations involving both charge conductivity and spin Hall conductivity, we report the SHRs of the III-V monolayer family, revealing an ultrahigh ratio of 0.58 in the hole-doped GaAs monolayer. In order to find more promising 2D materials, a descriptor for high SHR is proposed and applied to a high-throughput database, which provides the fully-relativistic band structures and Wannier Hamiltonians of 216 exfoliable monolayer semiconductors and has been released to the community. Among potential candidates for high SHR, the MXene monolayer Sc₂CCl₂ is identified with the proposed descriptor and confirmed by computation, demonstrating the descriptor validity for high SHR materials discovery.</li> </ul> <p> <span> Latest version: v2<br> Publication date: Sep 06, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.133"> Understanding the role of oxygen-vacancy defects in Cu₂O(111) from first-principle calculations</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:a8-4c" class="doi-right">10.24435/materialscloud:a8-4c </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Nanchen Dongfang, Marcella Iannuzzi, Yasmine Al-Hamdani </p> <ul class="list-inline"> <li class="text-justify">The presence of defects, such as copper and oxygen vacancies, in cuprous oxide films determines their characteristic carrier conductivity and consequently their application as semiconducting systems. There are still open questions on the induced electronic re-distribution, including the formation of polarons. Indeed, to accurately reproduce the structural and electronic properties at the cuprous oxide surface, very large slab models and theoretical approaches that go beyond the standard generalized gradient corrected density functional theory are needed. In this work we investigate oxygen vacancies formed in proximity of a reconstructed Cu₂O(111) surface, where the outermost unsaturated copper atoms are removed, thus forming non-stoichiometric surface layers with copper vacancies. We address simultaneously surface and bulk properties by modelling a thick and symmetric slab, to find that hybrid exchange-correlation functionals are needed to describe the oxygen vacancy in this ...</li> </ul> <p> <span> Latest version: v2<br> Publication date: Sep 04, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.132"> Automated computational workflows for muon spin spectroscopy</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:yy-ds" class="doi-right">10.24435/materialscloud:yy-ds </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Ifeanyi J. Onuorah, Miki Bonacci, Muhammad M. Isah, Marcello Mazzani, Roberto De Renzi, Giovanni Pizzi, Pietro Bonfà </p> <ul class="list-inline"> <li class="text-justify">Muon spin rotation and relaxation spectroscopy is a powerful tool for studying magnetic materials, offering a local probe that complements scattering techniques and provides advantages in cases of strong incoherent scattering or neutron absorption. By integrating computational methods (DFT+μ), the microscopic interactions driving the observed signals can be precisely quantified, enhancing the technique’s predictive power. We present a set of efficient algorithms and workflows - implemented in the AiiDA framework - that automate the DFT+μ procedure, where the muon is treated as a hydrogen impurity within the density functional theory framework. Our approach automates the identification of muon stopping sites, dipolar interactions, and hyperfine interactions. In this record we share the result of our calculations on well-known compounds, to demonstrate the accuracy and ease of use of our protocol.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Aug 30, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.131"> Ab-initio simulation of liquid water without artificial high temperature</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:89-2k" class="doi-right">10.24435/materialscloud:89-2k </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Chenyu Wang, Wei Tian, Ke Zhou </p> <ul class="list-inline"> <li class="text-justify">Comprehending the structure and dynamics of water is crucial in various fields such as water desalination, ion separation, electrocatalysis, and biochemical processes. While reported works show that the ab-initio molecular dynamics (AIMD) can accu- rately portray water’s structure, the artificial high temperature (AHT) from 120K to 30K is needed to mimic the quantum nature of hydrogen-bond network from GGA, metaGGA to hybrid functionals. The AHT proves to be an inadequate approach for systems involving aqueous multiphase mixtures, such as water-solid interfaces and aque- ous solutions. This is due to the activation of additional phonons in other phases, which can lead to an overestimation of the dynamics for nearby water molecules. In this work, we find the regularized SCAN (rSCAN) functional can well capture both the structure and dynamics of liquid water at ambient conditions without AHT. Moreover, rSCAN can well match the experimental results of hydration structures for alkali, ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Aug 29, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.130"> Prediction rigidities for data-driven chemistry</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:6x-gs" class="doi-right">10.24435/materialscloud:6x-gs </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Sanggyu Chong, Filippo Bigi, Federico Grasselli, Philip Loche, Matthias Kellner, Michele Ceriotti </p> <ul class="list-inline"> <li class="text-justify">The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency whilst guaranteeing interpretability and transferability. In this work, we demonstrate the wide utility of prediction rigidities, a family of metrics derived from the loss function, in understanding the robustness of ML model predictions. We show that the prediction rigidities allow the assessment of the model not only at the global level, but also on the local or the component-wise level at which the intermediate (e.g. atomic, body-ordered, or range-separated) predictions are made. We leverage these metrics to understand the learning behavior of different ML models, and to guide efficient dataset construction for model training. We finally implement the formalism for a ML model targeting a coarse-grained system to demonstrate the ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Aug 28, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.129"> High-throughput dataset of impurity adsorption on common catalysts in biomass upgrading applications</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:kr-m2" class="doi-right">10.24435/materialscloud:kr-m2 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Michelle A. Nolen, Sean A. Tacey, Martha A. Arellano-Treviño, Kurt M. Van Allsburg, Carrie A. Farberow </p> <ul class="list-inline"> <li class="text-justify">An extensive dataset consisting of adsorption energies of pernicious impurities present in biomass upgrading processes on common catalysts and support materials has been generated. This work aims to inform catalyst and process development for the conversion of biomass-derived feedstocks to fuels and chemicals. A high-throughput workflow was developed to execute density functional theory calculations for a diverse set of atomic (Al, B, Ca, Cl, Fe, K, Mg, Mn, N, Na, P, S, Si, Zn) and molecular (COS, H₂S, HCl, HCN, K₂O, KCl, NH₃) species on 35 unique surfaces for transition-metal (Ag, Au, Co, Cu, Fe, Ir, Ni, Pd, Pt, Re, Rh, Ru) and metal-oxide (Al₂O₃, MgO, anatase-TiO₂, rutile-TiO₂, ZnO, ZrO₂) catalysts and supports. Approximately 3,000 unique adsorption geometries were obtained. The data record includes structure and calculation output files for each unique adsorbate geometry on each surface.</li> </ul> <p> <span> Latest version: v2<br> Publication date: Aug 27, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.128"> Spectral operator representations</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:vm-5n" class="doi-right">10.24435/materialscloud:vm-5n </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Austin Zadoks, Antimo Marrazzo, Nicola Marzari </p> <ul class="list-inline"> <li class="text-justify">Materials are often represented in machine learning applications by (chemical-)geometric descriptions of their atomic structure. In this work, we propose an alternative framework for representing materials using descriptions of their electronic structure called Spectral Operator Representations (SOREPs). This record contains the code and data used to study carbon nanotubes (CNTs), barium titanate polymorphs, and the accelerated screening of transparent conducting materials with SOREPs. A data set for each application is provided: p<sub>z</sub> tight binding band structures for the three CNT configurations studied; the structures, band dispersions, and SOREP features of 127 BaTiO₃ polymorphs; and the SOREP features and ML targets for the MC3D materials considered in the accelerated screening. Additionally, code including patch files for Quantum ESPRESSO, the "sorep" python package, and the set of scripts used to prepare these data, train ML models, and plot results is provided.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Aug 26, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.127"> Tunable topological phases in nanographene-based spin-½ alternating-exchange Heisenberg chains</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:x8-7y" class="doi-right">10.24435/materialscloud:x8-7y </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Chenxiao Zhao, Gonçalo Catarina, Jin-Jiang Zhang, João Henriques, Lin Yang, Ji Ma, Xinliang Feng, Oliver Gröning, Pascal Ruffieux, Joaquín Rossier, Roman Fasel </p> <ul class="list-inline"> <li class="text-justify">Unlocking the potential of topological order within many-body spin systems has long been a central pursuit in the realm of quantum materials. Despite extensive efforts, the quest for a versatile platform enabling site-selective spin manipulation, essential for tuning and probing diverse topological phases, has persisted. Here, we utilize on-surface synthesis to construct spin-1/2 alternating-exchange Heisenberg (AH) chains with antiferromagnetic couplings J1 and J2 by covalently linking Clar's goblets -- nanographenes each hosting two antiferromagnetically-coupled unpaired electrons. In a recent work, utilizing scanning tunneling microscopy, we exert atomic-scale control over the spin chain lengths, parities and exchange-coupling terminations, and probe their magnetic response by means of inelastic tunneling spectroscopy. Our investigation confirms the gapped nature of bulk excitations in the chains, known as triplons. Besides, the triplon dispersion relation is successfully ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Aug 22, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.126"> Structural transitions of calcium carbonate by molecular dynamics simulation</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:ft-57" class="doi-right">10.24435/materialscloud:ft-57 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Elizaveta Sidler, Raffaela Cabriolu </p> <ul class="list-inline"> <li class="text-justify">Calcium carbonate (CaCO₃) plays a crucial role in the global carbon cycle, and its phase diagram is of significant scientific interest. We used molecular dynamics to investigate selected structural phase transitions of calcium carbonate. Using the Raiteri potential, we explored the structural transitions occurring at the constant pressure of 1 bar, with temperatures ranging from 300 to 2500 K, and at the constant temperature of 1600 K, with pressures ranging from 0 to 13 GPa. With increasing temperature, the transitions between calcite, CaCO₃-IV, and CaCO₃-V were characterized. In the calcite structure, the carbonate ions are ordered in a planar triangular arrangement, alternating with layers of calcium ions. As the temperature increases, the transition from calcite to CaCO₃-IV occurs, leading to partial disordering of the carbonate ions. At higher temperatures, CaCO₃-IV transforms into CaCO₃-V. Through free energy analysis, we classified the latter transition as a continuous ...</li> </ul> <p> <span> Latest version: v2<br> Publication date: Aug 21, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.124"> Substrate-aware computational design of two-dimensional materials</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:8q-a1" class="doi-right">10.24435/materialscloud:8q-a1 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Arslan Mazitov, Ivan Kruglov, Alexey V. Yanilkin, Aleksey V. Arsenin, Valentyn S. Volkov, Dmitry G. Kvashnin, Artem R. Oganov, Kostya S. Novoselov </p> <ul class="list-inline"> <li class="text-justify">Two-dimensional (2D) materials have attracted considerable attention due to their remarkable electronic, mechanical and optical properties, making them prime candidates for next-generation electronic and optoelectronic applications. Despite their widespread use in combination with substrates in practical applications, including the fabrication process and final device assembly, computational studies often neglect the effects of substrate interactions for simplicity. In this record, we provide the results of the computational study of the stable 2D molybdenum-sulfur (Mo-S) structures on a c-cut sapphire (Al₂O₃). In particular, we provide the results of the evolutionary search in the Mo-S / Al₂O₃ (0001) system, the machine learning interatomic potential (MLIP) used for local relaxation of the systems during the evolutionary search together with its training set, post-processing data on electronic and phonon band structures of the stable 2D Mo-S structures, and the predicted stability patterns from the perspective of CVD synthesis.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Aug 19, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.123"> Expanding density-correlation machine learning representations for anisotropic coarse-grained particles</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:mk-vn" class="doi-right">10.24435/materialscloud:mk-vn </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Arthur Lin, Kevin Huguenin-Dumittan, Yong-Cheol Cho, Jigyasa Nigam, Rose Cersonsky </p> <ul class="list-inline"> <li class="text-justify">This record contains three datasets and the scripts used to generate figures in "Expanding density-correlation machine learning representations for anisotropic coarse-grained particles." This paper explores the theory and implementation of machine-learning descriptors for ellipsoidal bodies, extending the popular "Smooth Overlap of Atomic Positions" (SOAP) formalism. These case studies serve to demonstrate the different use cases of this technology. The three datasets are: - Generated configurations of nematic and smectic liquid crystal systems, with a range of orientational order, characterized by the nematic order parameter - Dimers of (1, 1.5, 2) ellipsoids at different interaction cutoffs and rotations, with computed Gay-Berne type energies - Crystalline configurations of planar benzene molecules, with energetics computed using QuantumEspresso v7.046 using Perdew–Burke–Ernzerhof (PBE) pseudopotentials and cutoff parameters reported by Prandini et al., Grimme D3-dispersion correction, and a 3 × 3 Monkhorst–Pack k-point grid.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Aug 14, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.122"> Effect of hydrogen on the local chemical bonding states and structure of amorphous alumina by atomistic and electrostatic modeling of auger parameter shifts</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:9v-61" class="doi-right">10.24435/materialscloud:9v-61 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Simon Gramatte, Olivier Politano, Claudia Cancellieri, Ivo Utke, Lars Jeurgens, Vladyslav Turlo </p> <ul class="list-inline"> <li class="text-justify">This study discloses the effect of hydrogen impurities on the local chemical bonding states and structure of amorphous alumina films by predicting measured Auger parameter shifts using a combination of atomistic and electrostatic modeling. Different amorphous alumina polymorphs with variable H-content and density, as grown by atomic layer deposition, were successfully modeled using a universal machine learning interatomic potential. The annealing of highly defective crystalline hydroxide structures with experimental H-contents at the corresponding atomic layer deposition temperatures led to excellent agreement between theory and experiment in the density and structure of the resulting amorphous alumina polymorphs. The measured Auger parameter shifts of Al cations in such polymorphs were accurately predicted with respect to the H content by assuming that all H atoms are present in the form of hydroxyl ligands in the randomly interconnected 4-fold, 5-fold, and 6-fold ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Aug 12, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.121"> A dual-cutoff machine-learned potential for condensed organic systems obtained via uncertainty-guided active learning</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:ed-gp" class="doi-right">10.24435/materialscloud:ed-gp </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Leonid Kahle, Benoit Minisini, Tai Bui, Jeremy First, Corneliu Buda, Thomas Goldman, Erich Wimmer </p> <ul class="list-inline"> <li class="text-justify">Machine-learned potentials (MLPs) trained on ab initio data combine the computational efficiency of classical interatomic potentials with the accuracy and generality of the first-principles method used in the creation of the respective training set. In this work, we implement and train a MLP to obtain an accurate description of the potential energy surface and property predictions for organic compounds, as both single molecules and in the condensed phase. We devise a dual descriptor, based on the atomic cluster expansion (ACE), that couples an information-rich short-range description with a coarser long-range description that captures weak intermolecular interactions. We employ uncertainty-guided active learning for the training set generation, creating a dataset that is comparatively small for the breadth of application and consists of alcohols, alkanes, and an adipate. Utilizing that MLP, we calculate densities of those systems of varying chain lengths as a function of ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Aug 12, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.120"> Correlations of spin splitting and orbital fluctuations due to 1/f charge noise in the Si/SiGe Quantum Dot</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:91-mj" class="doi-right">10.24435/materialscloud:91-mj </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Marcin Kępa, Łukasz Cywiński, Jan A. Krzywda </p> <ul class="list-inline"> <li class="text-justify">Fluctuations of electric fields can change the position of a gate-defined quantum dot in a semiconductor heterostructure. In the presence of magnetic field gradient, these stochastic shifts of electron's wavefunction lead to fluctuations of electron's spin splitting. The resulting spin dephasing due to charge noise limits the coherence times of spin qubits in isotopically purified Si/SiGe quantum dots. We investigate the spin splitting noise caused by such process caused by microscopic motion of charges at the semiconductor-oxide interface. We compare effects of isotropic and planar displacement of the charges, and estimate their densities and typical displacement magnitudes that can reproduce experimentally observed spin splitting noise spectra. We predict that for defect density of 10¹⁰ cm⁻², visible correlations between noises in spin splitting and in energy of electron's ground state in the quantum dot, are expected.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Aug 07, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.118"> Simulation of 1/f charge noise affecting a quantum dot in a Si/SiGe structure</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:mx-0w" class="doi-right">10.24435/materialscloud:mx-0w </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Marcin Kępa, Niels Focke, Łukasz Cywiński, Jan A. Krzywda </p> <ul class="list-inline"> <li class="text-justify">Due to presence of magnetic field gradient needed for coherent spin control, dephasing of single-electron spin qubits in silicon quantum dots is often dominated by 1/f charge noise. We investigate theoretically fluctuations of ground state energy of an electron in gated quantum dot in realistic Si/SiGe structure. We assume that the charge noise is caused by motion of charges trapped at the semiconductor-oxide interface. We consider a realistic range of trapped charge densities, ρ∼10¹⁰ cm⁻², and typical lenghtscales of isotropically distributed displacements of these charges, δr≤1 nm, and identify pairs (ρ,δr) for which the amplitude and shape of the noise spectrum is in good agreement with spectra reconstructed in recent experiments on similar structures.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Aug 07, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.117"> From Methane to Methanol: Pd-iC-CeO2 Catalysts Engineered for High Selectivity via Mechano-Chemical Synthesis</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:dz-zz" class="doi-right">10.24435/materialscloud:dz-zz </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Juan D. Jiménez, Pablo G. Lustemberg, Maila Danielis, Estefanía Fernández-Villanueva, Sooyeon Hwang, Iradwikanari Waluyo, Adrian Hunt, Dominik Wierzbicki, Jie Zhang, Long Qi, Alessandro Trovarelli, Jose A. Rodriguez, Sara Colussi, M. Verónica Ganduglia-Pirovano, Sanjaya D. Senanayake </p> <ul class="list-inline"> <li class="text-justify">In the pursuit of selective conversion of methane directly to methanol in the liquid phase, a common challenge is the concurrent formation of undesirable liquid oxygenates or combustion byproducts. However, we demonstrate that monometallic Pd-CeO2 catalysts, modified by carbon, created by a simple mechanochemical synthesis method exhibit 100% selectivity towards methanol at 75°C, using hydrogen peroxide as oxidizing agent. The solvent-free synthesis yields a distinctive Pd-iC-CeO2 interface, where interfacial carbon (iC) modulates metal-oxide interactions and facilitates tandem methane activation and peroxide decomposition, thus resulting in an exclusive methanol selectivity of 100% with a rate of 117 µmol/gcat at 75°C. Notably, solvent interactions of H2O2 (aq) were found to be critical for methanol selectivity through a DFT-simulated Eley-Rideal-like mechanism. This mechanism uniquely enables the direct conversion of methane into methanol via a solid-liquid-gas process.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Aug 06, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.116"> High-throughput computation of Raman spectra from first principles</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:pg-h3" class="doi-right">10.24435/materialscloud:pg-h3 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Mohammad Bagheri, Hannu-Pekka Komsa </p> <ul class="list-inline"> <li class="text-justify">Raman spectroscopy is a widely-used non-destructive material characterization method, which provides information about the vibrational modes of the material and therefore of its atomic structure and chemical composition. Raman spectra can be simulated using atomistic first-principles methods but these are computationally demanding and thus the existing databases of computational Raman spectra are fairly small. We developed an optimized workflow to efficiently calculate the Raman tensors, from which the Raman spectra can be straightforwardly simulated. The workflow was benchmarked and validated by comparison to experiments and previous computational methods for select technologically relevant material systems. Using the workflow, we performed high-throughput calculations for a large set of materials (5099) belonging to many different material classes, and collected the results to a database.</li> </ul> <p> <span> Latest version: v2<br> Publication date: Aug 05, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.115"> Synchronized crystallization in tin-lead perovskite solar cells</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:kv-g1" class="doi-right">10.24435/materialscloud:kv-g1 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Yao Zhang, Chunyan Li, Haiyan Zhao, Zhongxun Yu, Xiaoan Tang, Jixiang Zhang, Zhenhua Chen, Jianrong Zeng, Peng Zhang, Liyuan Han, Han Chen </p> <ul class="list-inline"> <li class="text-justify">Tin-lead halide perovskites with a bandgap near 1.2 electron-volt hold great promise for thin-film photovoltaics. However, the film quality of solution-processed Sn-Pb perovskites is compromised by the asynchronous crystallization behavior between Sn and Pb components, where the crystallization of Sn-based perovskites tends to occur faster than that of Pb. Here we show that the rapid crystallization of Sn is rooted in its stereochemically active lone pair, which impedes coordination between the metal ion and Lewis base ligands in the perovskite precursor. From this perspective, we introduce a noncovalent binding agent targeting the open metal site of coordinatively unsaturated Sn(II) solvates, thereby synchronizing crystallization kinetics and homogenizing Sn-Pb alloying. The resultant single-junction Sn-Pb perovskite solar cells achieve a certified power conversion efficiency of 24.13 per cent. The encapsulated device retains 90 per cent of the initial efficiency after 795 hours ...</li> </ul> <p> <span> Latest version: v2<br> Publication date: Aug 05, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.114"> Optical materials discovery and design with federated databases and machine learning</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:5p-vq" class="doi-right">10.24435/materialscloud:5p-vq </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Victor Trinquet, Matthew L. Evans, Cameron Hargreaves, Pierre-Paul De Breuck, Gian-Marco Rignanese </p> <ul class="list-inline"> <li class="text-justify">Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are increasingly tabulated in open databases. The OPTIMADE API is a standardised format for representing crystal structures, their measured and computed properties, and the methods for querying and filtering them from remote resources. Currently, the OPTIMADE federation spans over 20 data providers, rendering over 30 million structures accessible in this way, many of which are novel and have only recently been suggested by machine learning-based approaches. In this work, we outline our approach to non-exhaustively screen this dynamic trove of structures for the next-generation of optical materials. By applying MODNet, a neural network-based model for property prediction that has been shown to perform especially well for small materials datasets, within a combined active learning and high-throughput computation ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Aug 05, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.112"> 2D pentagonal-graphene and pentagonal-silicene sheets engineered for the detection of DNA nucleobases for genetic biomarker: A DFT study</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:cw-ct" class="doi-right">10.24435/materialscloud:cw-ct </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Arzoo Hassan, Andleeb Mehmood, Umer Younis, Mahmood Ahmad Ghazanfar, Yong Wang, Xiaoqing Tian, Qing-Feng Sun </p> <ul class="list-inline"> <li class="text-justify">The deposited structure models of DNA adsorbed on prisitne PG/p-Si and metal (Au/W) doped PG/p-Si sheets, have been subjected to first principles calculations based on DFT (PBE+vdW). The calculated binding affinity on PG and p-Si surface by M062X/6-31G* level of theory and adsorption energies by DFT predicts that PG has higher sensitivity towards DNA nucleic bases compared to p-Si with evident changes in their band structure properties.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Jul 26, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.111"> Machine learning enables the discovery of 2D Invar and anti-Invar monolayers</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:hc-zb" class="doi-right">10.24435/materialscloud:hc-zb </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Shun Tian, Ke Zhou, Wanjian Yin, Yilun Liu </p> <ul class="list-inline"> <li class="text-justify">Materials demonstrating positive thermal expansion (PTE) or negative thermal expansion (NTE) are quite common, whereas those exhibiting zero thermal expansion (ZTE) are notably scarce. In this work, we identify the mechanical descriptors, namely in-plane tensile stiffness and out-of-plane bending stiffness, that can effectively classify PTE and NTE 2D crystals. By utilizing high throughput calculations and the state-of-the-art symbolic regression method, these descriptors aid in the discovery of ZTE or 2D Invar monolayers with the linear thermal expansion coefficient (LTEC) within ±2×10⁻⁶ K⁻¹ in the middle range of temperatures. Additionally, the descriptors assist the discovery of large PTE and NTE 2D monolayers with the LTEC larger than ±15×10⁻⁶ K⁻¹, which are so-called 2D anti-Invar monolayers. Advancing our understanding of materials with exceptionally low or high thermal expansion is of substantial scientific and technological interest, particularly in developing next-generation electronics at the nanometer even Ångstrom scale.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Jul 25, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.110"> Scaling relations and dynamical predictiveness of electric dipole strength on 2e- ORR catalytic property</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:kj-td" class="doi-right">10.24435/materialscloud:kj-td </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Wei Zhang, Zhijun Wu, Yin-xiao Sheng, Fu-li Sun, Wen-xian Chen, Gui-lin Zhuang </p> <ul class="list-inline"> <li class="text-justify">Efficient O₂ reduction to H₂O₂, vital for energy conversion and environmental cleanup, relies on precise control of heterogeneous catalysts interacting with reaction species. Through high-throughput density functional theory calculations, consisting of 369 single atom catalysts, we identified the polarized descriptor (electric dipole strength) on two-dimensional carbon materials, revealing insights into the catalytic effect of support polarization. Surprisingly, this descriptor exhibits advanced scaling relationships towards H₂O₂ synthesis, incorporating factors such as active metals, coordination environments, and surface curvatures, highlighting its widespread significance. Furthermore, it demonstrates reliable predictability for O₂ adsorption in dynamic water environments, with optimal reactivity observed within the range of -1.40 to -1.00 e·Å, as confirmed by dynamic and static simulations of the 2e- pathway of O₂ reduction. In essence, these findings offer valuable insights ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Jul 23, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.109"> Deterministic grayscale nanotopography to engineer mobilities in strained MoS₂ FETs</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:j5-7n" class="doi-right">10.24435/materialscloud:j5-7n </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Xia Liu, Berke Erbas, Ana Conde Rubio, Norma Rivano, Zhenyu Wang, Jin Jiang, Siiri Bienz, Naresh Kumar, Thibault Sohier, Marcos Penedo, Mitali Banerjee, Georg Fantner, Renato Zenobi, Nicola Marzari, Andras Kis, Giovanni Boero, Juergen Brugger </p> <ul class="list-inline"> <li class="text-justify">Field-effect transistors (FETs) based on two-dimensional materials (2DMs) with atomically thin channels have emerged as a promising platform for beyond-silicon electronics. However, low carrier mobility in 2DM transistors driven by phonon scattering remains a critical challenge. To address this issue, we propose the controlled introduction of localized tensile strain as an effective mean to inhibit electron-phonon scattering in 2DM. Strain is achieved by conformally adhering the 2DM via van-der-Waals forces to a dielectric layer previously nanoengineered with a gray-tone topography. Our results show that monolayer MoS₂ FETs under tensile strain achieve an 8-fold increase in on-state current, reaching mobilities of 185 cm²/Vs at room temperature, in good agreement with theoretical calculations. The present work on nanotopographic grayscale surface engineering and the use of high-quality dielectric materials has the potential to find application in the nanofabrication of photonic ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Jul 22, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.108"> Phonon-limited mobility for electrons and holes in highly-strained silicon</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:sy-4g" class="doi-right">10.24435/materialscloud:sy-4g </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Nicolas Roisin, Guillaume Brunin, Gian-Marco Rignanese, Denis Flandre, Jean-Pierre Raskin, Samuel Poncé </p> <ul class="list-inline"> <li class="text-justify">Strain engineering is a widely used technique for enhancing the mobility of charge carriers in semiconductors, but its effect is not fully understood. In this work, we perform first-principles calculations to explore the variations of the mobility of electrons and holes in silicon upon deformation by uniaxial strain up to 2% in the [100] crystal direction. We compute the π₁₁ and π₁₂ electron piezoresistances based on the low-strain change of resistivity with temperature in the range 200 K to 400 K, in excellent agreement with experiment. We also predict them for holes which were only measured at room temperature. Remarkably, for electrons in the transverse direction, we predict a minimum room-temperature mobility about 1200 cm²/Vs at 0.3% uniaxial tensile strain while we observe a monotonous increase of the longitudinal transport, reaching a value of 2200 cm²/Vs at high strain. We confirm these findings experimentally using four-point bending measurements, establishing the ...</li> </ul> <p> <span> Latest version: v4<br> Publication date: Jul 19, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.107"> Machine learning potential for the Cu-W system</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:1m-0s" class="doi-right">10.24435/materialscloud:1m-0s </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Manura Liyanage, Vladyslav Turlo, W. A. Curtin </p> <ul class="list-inline"> <li class="text-justify">Combining the excellent thermal and electrical properties of Cu with the high abrasion resistance and thermal stability of W, Cu-W nanoparticle-reinforced metal matrix composites and nano-multilayers (NMLs) are finding applications as brazing fillers and shielding material for plasma and radiation. Due to the large lattice mismatch between fcc Cu and bcc W, these systems have complex interfaces that are beyond the scales suitable for ab initio methods, thus motivating the development of chemically accurate interatomic potentials. Here, a neural network potential (NNP) for Cu-W is developed within the Behler-Parrinello framework using a curated training dataset that captures metallurgically-relevant local atomic environments. The Cu-W NNP accurately predicts (i) the metallurgical properties (elasticity, stacking faults, dislocations, thermodynamic behavior) in elemental Cu and W, (ii) energies and structures of Cu-W intermetallics and solid solutions, and (iii) a range of fcc ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Jul 18, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.106"> Low-energy modeling of three-dimensional topological insulator nanostructures</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:mx-bn" class="doi-right">10.24435/materialscloud:mx-bn </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Eduárd Zsurka, Cheng Wang, Julian Legendre, Daniele Di Miceli, Llorenç Serra, Detlev Grützmacher, Thomas L. Schmidt, Philipp Rüßmann, Kristof Moors </p> <ul class="list-inline"> <li class="text-justify">We develop an accurate nanoelectronic modeling approach for realistic three-dimensional topological insulator nanostructures and investigate their low-energy surface-state spectrum. Starting from the commonly considered four-band k·p bulk model Hamiltonian for the Bi₂Se₃ family of topological insulators, we derive new parameter sets for Bi₂Se₃, Bi₂Te₃ and Sb₂Te₃. We consider a fitting strategy applied to ab initio band structures around the Γ point that ensures a quantitatively accurate description of the low-energy bulk and surface states, while avoiding the appearance of unphysical low-energy states at higher momenta, something that is not guaranteed by the commonly considered perturbative approach. We analyze the effects that arise in the low-energy spectrum of topological surface states due to band anisotropy and electron-hole asymmetry, yielding Dirac surface states that naturally localize on different side facets. In the thin-film limit, when surface states hybridize ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Jul 05, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.104"> Inverse design of singlet fission materials with uncertainty-controlled genetic optimization</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:yn-vz" class="doi-right">10.24435/materialscloud:yn-vz </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Luca Schaufelberger, J. Terence Blaskovits, Ruben Laplaza, Clemence Corminboeuf, Kjell Jorner </p> <ul class="list-inline"> <li class="text-justify">Singlet fission has shown potential for boosting the power conversion efficiency of solar cells, but the scarcity of suitable molecular materials hinders its implementation. We introduce an uncertainty-controlled genetic algorithm (ucGA) based on ensemble machine learning predictions from different molecular representations that concurrently optimizes excited state energies, synthesizability, and singlet exciton size for the discovery of singlet fission materials. We show that uncertainty in the model predictions can control how far the genetic optimization moves away from previously known molecules. Running the ucGA in an exploitative setup performs local optimization on variations of known singlet fission scaffolds, such as acenes. In an explorative mode, hitherto unknown candidates displaying excellent excited state properties for singlet fission are generated. We suggest a class of heteroatom-rich mesoionic compounds as acceptors for charge-transfer mediated singlet fission. ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Jul 04, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.101"> Charge state-dependent symmetry breaking of atomic defects in transition metal dichalcogenides</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:jc-sx" class="doi-right">10.24435/materialscloud:jc-sx </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Feifei Xiang, Lysander Huberich, Preston A. Vargas, Riccardo Torsi, Jonas Allerbeck, Anne Marie Z. Tan, Chengye Dong, Pascal Ruffieux, Roman Fasel, Oliver Gröning, Yu-Chuan Lin, Richard G. Hennig, Joshua A. Robinson, Bruno Schuler </p> <ul class="list-inline"> <li class="text-justify">The functionality of atomic quantum emitters is intrinsically linked to their host lattice coordination. Structural distortions that spontaneously break the lattice symmetry strongly impact their optical emission properties and spin-photon interface. In a recent manuscript, we report on the direct imaging of charge state-dependent symmetry breaking of two prototypical atomic quantum emitters in mono- and bilayer MoS₂ by scanning tunneling microscopy (STM) and non-contact atomic force microscopy (nc-AFM). By changing the built-in substrate chemical potential, different charge states of sulfur vacancies (Vac<sub>S</sub>) and substitutional rhenium dopants (Re<sub>Mo</sub>) can be stabilized. Vac<sub>S</sub>⁻¹ as well as Re<sub>Mo</sub>⁰ and Re<sub>Mo</sub>⁻¹ exhibit local lattice distortions and symmetry-broken defect orbitals attributed to a Jahn-Teller effect (JTE) and pseudo-JTE, respectively. By mapping the electronic and geometric structure of single point defects, we ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Jul 03, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.100"> Automated prediction of ground state spin for transition metal complexes</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:jx-a5" class="doi-right">10.24435/materialscloud:jx-a5 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Yuri Cho, Ruben Laplaza, Sergi Vela, Clemence Corminboeuf </p> <ul class="list-inline"> <li class="text-justify">Predicting the ground state spin of transition metal complexes is a challenging task. Previous attempts have been focused on specific regions of chemical space, whereas a more general automated approach is required to process crystallographic structures for high-throughput quantum chemistry computations. In this work, we developed a method to predict ground state spins of transition metal complexes. We started by constructing a dataset which contains 2,063 first row transition metal complexes taken from experimental crystal structures and their computed ground state spins. This dataset showed large chemical diversity in terms of metals, metal oxidation states, coordination geometries, and ligands. Then, we analyzed the trends between structural and electronic features of the complexes and their ground state spins, and put forward an empirical spin state assignment model. We also used simple descriptors to build a statistical model with >95% predictive accuracy across the board. ...</li> </ul> <p> <span> Latest version: v2<br> Publication date: Jul 01, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.96"> Water slowing down drives the occurrence of the low temperature dynamical transition in microgels</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:6n-zd" class="doi-right">10.24435/materialscloud:6n-zd </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Letizia Tavagnacco, Marco Zanatta, Elena Buratti, Monica Bertoldo, Ester Chiessi, Markus Appel, Francesca Natali, Andrea Orecchini, Emanuela Zaccarelli </p> <ul class="list-inline"> <li class="text-justify">The protein dynamical transition marks an increase in atomic mobility and the onset of anharmonic motions at a critical temperature, which is considered relevant for protein functionality. This phenomenon is ubiquitous, regardless of protein composition, structure and biological function and typically occurs at large protein content, to avoid water crystallization. Recently, a dynamical transition has also been reported in non-biological macromolecules, such as poly(N-isopropyl acrylamide) (PNIPAM) microgels, bearing many similarities to proteins. While the generality of this phenomenon is well-established, the role of water in the transition remains a subject of debate. In this study, we use atomistic molecular dynamics simulations and elastic incoherent neutron scattering (EINS) experiments with selective deuteration to investigate the microscopic origin of the dynamical transition and distinguish water and PNIPAM roles. While a standard analysis of EINS experiments would ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Jun 24, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.95"> Doping-Induced Electronic and Structural Phase Transition in the Bulk Weyl semimetal Mo<sub>1-x</sub>W<sub>x</sub>Te<sub>2</sub></a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:ks-0h" class="doi-right">10.24435/materialscloud:ks-0h </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> O. Fedchenko, F. K. Diekmann, P. Rüßmann, M. Kallmayer, L. Odenbreit, S. M. Souliou, M. Frachet, A. Winkelmann, M. Merz, S. Chernov, D. Vasilyev, D. Kutnyakhov, O. Tkach, Ya. Lytvynenko, K. Medjanik, C. Schlueter, A. Gloskovskii, T. R. F. Peixoto, M. Hoesch, M. Le Tacon, Y. Mokrousov, K. Roßnagel, G. Schönhense, H.-J. Elmers </p> <ul class="list-inline"> <li class="text-justify">A comprehensive study of the electronic and structural phase transition from 1T` to T<sub>d</sub> in the bulk Weyl semimetal Mo<sub>1-x</sub>W<sub>x</sub>Te<sub>2</sub> at different doping concentrations has been carried out using time-of-flight momentum microscopy (including circular and linear dichroism), X-ray photoelectron spectroscopy, X-ray photoelectron diffraction, X-ray diffraction (XRD), angle-resolved Raman spectroscopy, transport measurements (including longitudinal elastoresistance), density functional theory (DFT) and Kikuchi pattern calculations. High-resolution, angle-resolved photoemission spectroscopy at 20 K reveals surface electronic states, which are indicative for topological Fermi arcs. Their dispersion agrees with the position of Weyl points predicted by DFT calculations based on the precise crystal structure of our samples obtained from XRD measurements. Raman spectroscopy confirms the inversion symmetry breaking for the T<sub>d</sub>-phase, which is a ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Jun 24, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.93"> Designing bifunctional perovskite catalysts for the oxygen reduction and evolution reactions</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:q7-74" class="doi-right">10.24435/materialscloud:q7-74 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Casey E. Beall, Emiliana Fabbri, Adam H. Clark, Vivian Meier, Nur Sena Yüzbasi, Thomas Graule, Sayaka Takahashi, Yuto Shirase, Makoto Uchida, Thomas J. Schmidt </p> <ul class="list-inline"> <li class="text-justify">The development of unified regenerative fuel cells (URFC) necessitates an active and stable bifunctional oxygen electrocatalyst. The unique challenge of possessing high activity for both the oxygen reduction (ORR) and oxygen evolution (OER) reactions, while maintaining stability over a wide potential window impedes the design of bifunctional oxygen electrocatalysts. Herein, two design strategies are explored to optimize their performance. The first incorporates active sites for ORR and OER, Mn and Co, into a single perovskite structure, which is achieved with the perovskites Ba<sub>0.5</sub>Sr<sub>0.5</sub>Co<sub>0.8</sub>Mn<sub>0.2</sub>O<sub>3-δ</sub> (BSCM) and La<sub>0.5</sub>Ba<sub>0.25</sub>Sr<sub>0.25</sub>Co<sub>0.5</sub>Mn<sub>0.5</sub>O<sub>3-δ</sub> (LBSCM). The second combines an active ORR perovskite catalyst (La<sub>0.4</sub>Sr<sub>0.6</sub>MnO<sub>3-δ</sub> (LSM)) with an OER active perovskite catalyst ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Jun 21, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.92"> Uncovering the origin of interface stress enhancement and compressive-to-tensile stress transition in immiscible nanomultilayers</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:8a-gh" class="doi-right">10.24435/materialscloud:8a-gh </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Yang Hu, Giacomo Lorenzin, Jeyun Yeom, Manura Liyanage, William Curtin, Lars Jeurgens, Jolanta Janczak-Rusch, Claudia Cancellieri, Vladyslav Turlo </p> <ul class="list-inline"> <li class="text-justify">The intrinsic stress in nanomultilayers (NMLs) is typically dominated by interface stress, which is particularly high in immiscible Cu/W NMLs. Here, atomistic simulations with a chemically-accurate neural network potential reveal the role of interfacial intermixing and metastable phase formation on the interface stress levels. These results rationalize an experimentally-reported compressive-to-tensile transition as a function of NML deposition conditions and the extremely high interface stresses under some conditions.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Jun 21, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.91"> Interplay between ferroelectricity and metallicity in hexagonal YMnO₃</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:ep-pr" class="doi-right">10.24435/materialscloud:ep-pr </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Tara Niamh Tosic, Yuting Chen, Nicola Ann Spaldin </p> <ul class="list-inline"> <li class="text-justify">We use first-principles density functional theory to investigate how the polar distortion is affected by doping in multiferroic hexagonal yttrium manganite, h-YMnO₃. While the introduction of charge carriers tends to suppress the polar distortion in conventional ferroelectrics, the behavior in improper geometric ferroelectrics, of which h-YMnO₃ is the prototype, has not been studied to date. Using both background charge doping and atomic substitution, we find an approximately linear dependence of the polar distortion on doping concentration, with hole doping reducing and electron doping enhancing it. We show that this behavior is a direct consequence of the improper geometric nature of the ferroelectricity. In addition to its doping effect, atomic substitution can further suppress or enhance the polar distortion through changes in the local chemistry and geometry.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Jun 21, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.90"> DFT calculations of the electronic structure of CoPt in L1₁ and A1 structures</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:m4-b5" class="doi-right">10.24435/materialscloud:m4-b5 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Tenghua Gao, Philipp Rüßmann, Qianwen Wang, Hiroki Hayashi, Dongwook Go, Song Zhang, Takashi Harumoto, Rong Tu, Lianmeng Zhang, Yuriy Mokrousov, Ji Shi, Kazuya Ando </p> <ul class="list-inline"> <li class="text-justify">Spintronics applications for high-density non-volatile memories require simultaneous optimization of the perpendicular magnetic anisotropy (PMA) and current-induced magnetization switching. These properties determine, respectively, the thermal stability of a ferromagnetic memory cell and a low operation power consumption, which are mutually incompatible with the spin transfer torque as the driving force for the switching. Here, we demonstrate a strategy of alloy engineering to overcome this obstacle by using electrically induced orbital currents instead of spin currents. A non-equilibrium orbital density generated in paramagnetic γ-FeMn flows into CoPt coupled to the magnetization through spin-orbit interaction, ultimately creating an orbital torque. Controlling the atomic arrangement of Pt and Co by structural phase transition, we show that the propagation length of the transferred angular momentum can be modified concurrently with the PMA strength. We find a strong correlation ...</li> </ul> <p> <span> Latest version: v2<br> Publication date: Jun 20, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.89"> Nuclear quantum effects on the electronic structure of water and ice</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:pd-j6" class="doi-right">10.24435/materialscloud:pd-j6 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Margaret Berrens, Arpan Kundu, Marcos F. Calegari Andrade, Tuan Anh Pham, Giulia Galli, Davide Donadio </p> <ul class="list-inline"> <li class="text-justify">The electronic properties and optical response of ice and water are intricately shaped by their molecular structure, including the quantum mechanical nature of hydrogen atoms. Despite numerous former studies, a comprehensive understanding of nuclear quantum effects (NQE) on the electronic structure of water and ice at finite temperatures remains elusive. Here, we utilize molecular simulations that harness efficient machine-learning potentials and many-body perturbation theory to assess how NQEs impact the electronic bands of water and hexagonal ice. By comparing path-integral and classical simulations, we find that NQEs lead to a larger renormalization of the fundamental gap of ice, compared to that of water, ultimately yielding similar bandgaps in the two systems, consistent with experimental estimates. Our calculations suggest that the increased quantum mechanical delocalization of protons in ice, relative to water, is a key factor leading to the enhancement of NQEs on the electronic structure of ice.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Jun 17, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.88"> Computational Design of Transition Metal Catalysts for Hydrodefluorination of Trifluoromethylarenes using Hydrosilane</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:h6-fj" class="doi-right">10.24435/materialscloud:h6-fj </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Thanapat Worakul, Boodsarin Sawatlon, Panida Surawatanawong </p> <ul class="list-inline"> <li class="text-justify">The C-F activation is one of the important processes in chemical synthesis. Here, we studied the hydrodefluorination of PhCF3 with SiMe2Ph-H catalyzed by Ni(0) complexes. The mechanisms involve three main steps: C-F bond cleavage of PhCF3 on the nickel complex, transmetalation of Ni-F with SiMe2Ph-H to form a nickel hydride complex, and C-H reductive elimination of PhCF2H. We performed density functional calculations on nickel complexes with thirty carbene and phosphine ligands to obtain the relative free energy profiles. Then, linear free energy scaling relationships were determined and molecular volcano plots were constructed. To accurately describe catalytic activity, we found that multiple reference states must be considered. Thus, the concept of "reference-generalized volcano plots (RGVPs)" was introduced to assist with the selection of the appropriate reference state to determine catalytic activity. Our regression models indicate that electronic properties of ligands ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Jun 14, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.87"> Second-harmonic generation tensors from high-throughput density-functional perturbation theory</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:w5-d6" class="doi-right">10.24435/materialscloud:w5-d6 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Victor Trinquet, Francesco Naccarato, Guillaume Brunin, Guido Petretto, Ludger Wirtz, Geoffroy Hautier, Gian-Marco Rignanese </p> <ul class="list-inline"> <li class="text-justify">Optical materials play a key role in enabling modern optoelectronic technologies in a wide variety of domains such as the medical or the energy sector. Among them, nonlinear optical crystals are of primary importance to achieve a broader range of electromagnetic waves in the devices. However, numerous and contradicting requirements significantly limit the discovery of new potential candidates, which, in turn, hinders the technological development. In the present work, the static nonlinear susceptibility and dielectric tensor are computed via density functional perturbation theory for a set of 579 inorganic semiconductors. The aim of this work is to provide a relevant dataset to foster the identification of promising nonlinear optical crystals in order to motivate their subsequent experimental investigation.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Jun 13, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.85"> Effect of residual stress and microstructure on mechanical properties of sputter-grown Cu/W nanomultilayers</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:nn-03" class="doi-right">10.24435/materialscloud:nn-03 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Giacomo Lorenzin, Fedor Klimashin, Jeyun Jeom, Yang Hu, Johann Michler, Jolanta Janczak-Rusch, Vladyslav Turlo, Claudia Cancellieri </p> <ul class="list-inline"> <li class="text-justify">The combination of the high wear resistance and mechanical strength of W with the high thermal conductivity of Cu makes the Cu/W system an attractive candidate material for heat sink plasma and radiation tolerance applications. However, the resulting mechanical properties of multilayers and coatings strongly depend on the microstructure of the layers. In this work, the mechanical properties of Cu/W nanomultilayers with different densities of internal interfaces are systematically investigated for two opposite in-plane stress states and critically discussed in comparison with literature. Atomistic simulations with the state-of-the-art neural network potential are used to explain the experimental findings. The results suggest that the microstructure, specifically the excess free volume associated with porosity and interface disorder interconnected with the stress state, has a great impact on the mechanical properties, notably Young's modulus of Cu/W nanomultilayers.</li> </ul> <p> <span> Latest version: v1<br> Publication date: Jun 07, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.84"> Temperature-invariant crystal-glass heat conduction: from meteorites to refractories</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:3k-v7" class="doi-right">10.24435/materialscloud:3k-v7 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Michele Simoncelli, Daniele Fournier, Massimiliano Marangolo, Etienne Balan, Keevin Béneut, Benoit Baptiste, Béatrice Doisneau, Nicola Marzari, Francesco Mauri </p> <ul class="list-inline"> <li class="text-justify">The thermal conductivities of crystals and glasses vary strongly and with opposite trends upon heating, decreasing in crystals and increasing in glasses. Here, we show---with first-principles predictions based on the Wigner transport equation and thermoreflectance experiments---that the dominant transport mechanisms of crystals (particle-like propagation) and glasses (wave-like tunnelling) can coexist and compensate in materials with crystalline bond order and nearly glassy bond geometry. We demonstrate that ideal compensation emerges in silica tridymite, carved from a meteorite found in Steinbach (Germany) in 1724, and yields a ‘Propagation-Tunneling-Invariant’ (PTI) conductivity that is independent of temperature and intermediate between the opposite trends of α-quartz crystal and silica glass. We show how such PTI conductivity occurs in the quantum regime below the Debye temperature, and can largely persist at high temperatures in a geometrically amorphous tridymite phase found ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Jun 07, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.83"> Spin-dependent interactions in orbital-density-dependent functionals: non-collinear Koopmans spectral functionals</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:kp-2v" class="doi-right">10.24435/materialscloud:kp-2v </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Antimo Marrazzo, Nicola Colonna </p> <ul class="list-inline"> <li class="text-justify">The presence of spin-orbit coupling or non-collinear magnetic spin states can have dramatic effects on the ground-state and spectral properties of materials, in particular on the band structure. Here, we develop non-collinear Koopmans-compliant functionals based on Wannier functions and density-functional perturbation theory, targeting accurate spectral properties in the quasiparticle approximation. Our non-collinear Koopmans-compliant theory involves functionals of four-component orbitals densities, that can be obtained from the charge and spin-vector densities of Wannier functions. We validate our approach on four emblematic non-magnetic and magnetic semiconductors where the effect of spin-orbit coupling goes from small to very large: the III-IV semiconductor GaAs, the transition-metal dichalcogenide WSe₂, the cubic perovskite CsPbBr₃, and the ferromagnetic semiconductor CrI₃. The predicted band gaps are comparable in accuracy to state-of-the-art many-body perturbation theory ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: Jun 03, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.82"> Density functional perturbation theory for one-dimensional systems: implementation and relevance for phonons and electron-phonon interactions</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:gn-qs" class="doi-right">10.24435/materialscloud:gn-qs </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Norma Rivano, Nicola Marzari, Thibault Sohier </p> <ul class="list-inline"> <li class="text-justify">The electronic and vibrational properties and electron-phonon couplings of one-dimensional materials will be key to many prospective applications in nanotechnology. Dimensionality strongly affects these properties and has to be correctly accounted for in first-principles calculations. Here we develop and implement a formulation of density-functional and density-functional perturbation theory that is tailored for one-dimensional systems. A key ingredient is the inclusion of a Coulomb cutoff, a reciprocal-space technique designed to correct for the spurious interactions between periodic images in periodic-boundary conditions. This restores the proper one-dimensional open-boundary conditions, letting the true response of the isolated one-dimensional system emerge. In addition to total energies, forces and stress tensors, phonons and electron-phonon interactions are also properly accounted for. We demonstrate the relevance of the present method on a portfolio of realistic systems: BN ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: May 31, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.80"> Solvation free energies from machine learning molecular dynamics</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:a0-jh" class="doi-right">10.24435/materialscloud:a0-jh </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Nicephore Bonnet, Nicola Marzari </p> <ul class="list-inline"> <li class="text-justify">In this paper, we propose an extension to the approach of [Xi, C; et al. J. Chem. Theory Comput. 2022, 18, 6878] to calculate ion solvation free energies from first-principles (FP) molecular dynamics (MD) simulations of a hybrid solvation model. The approach is first re-expressed within the quasi-chemical theory of solvation. Then, to allow for longer simulation times than the original first-principles molecular dynamics approach and thus improve the convergence of statistical averages at a fraction of the original computational cost, a machine-learned (ML) energy function is trained on FP energies and forces and used in the MD simulations. The ML workflow and MD simulation times (≈200 ps) are adjusted to converge the predicted solvation energies within a chemical accuracy of 0.04 eV. The extension is successfully benchmarked on the same set of alkaline and alkaline-earth ions. The record includes all molecular-dynamics trajectories, energies and forces used to obtain the ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: May 27, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.79"> Tailoring magnetism of graphene nanoflakes via tip-controlled dehydrogenation</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:yh-fj" class="doi-right">10.24435/materialscloud:yh-fj </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Chenxiao Zhao, Qiang Huang, Leoš Valenta, Kristjan Eimre, Lin Yang, Aliaksandr V. Yakutovich, Wangwei Xu, Xinliang Feng, Michal Juríček, Roman Fasel, Pascal Ruffieux, Carlo A. Pignedoli </p> <ul class="list-inline"> <li class="text-justify">Atomically precise graphene nanoflakes called nanographenes have emerged as a promising platform to realize carbon magnetism. Their ground state spin configuration can be anticipated by Ovchinnikov-Lieb rules based on the mismatch of π electrons from two sublattices. While rational geometrical design achieves specific spin configurations, further direct control over the π electrons offers a desirable extension for efficient spin manipulations and potential quantum device operations. To this end, in a recent publication, we applied a site-specific dehydrogenation using a scanning tunneling microscope tip to nanographenes deposited on a Au(111) substrate, which showed the capability of precisely tailoring the underlying π-electron system and therefore efficiently manipulating their magnetism. Through first-principles calculations and tight-binding meanfield-Hubbard modeling, we demonstrated that the dehydrogenation-induced Au—C bond formation along with the resulting hybridization ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: May 23, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.78"> Emergent half-metal with mixed structural order in (111)-oriented (LaMnO₃)₂ₙ|(SrMnO₃)ₙ superlattices</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:4f-j1" class="doi-right">10.24435/materialscloud:4f-j1 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Fabrizio Cossu, Jùlio Alves Do Nascimento, Stuart A. Cavill, Igor Di Marco, Vlado K. Lazarov, Heung-Sik Kim </p> <ul class="list-inline"> <li class="text-justify">Using first-principles techniques, we study the structural, magnetic, and electronic properties of (111)-oriented (LaMnO₃)₂ₙ|(SrMnO₃)ₙ superlattices of varying thickness (n=2,4,6). We find that the properties of the thinnest superlattice (n=2) are similar to the celebrated half-metallic ferromagnetic alloy La<sub>2/3</sub>Sr<sub>1/3</sub>⁢MnO₃, with quenched Jahn-Teller distortions. At intermediate thickness (n=4), the a⁻a⁻a⁻ tilting pattern transitions to the a⁻a⁻c⁺ tilting pattern, driven by the lattice degrees of freedom in the LaMnO₃ region. The emergence of the Jahn-Teller modes and the spatial extent needed for their development play a key role in this structural transition. For the largest thickness considered (n=6), we unveil an emergent separation of Jahn-Teller and volume-breathing orders in the ground-state structure with the a⁻a⁻c⁺ tilting pattern, whereas it vanishes in the antiferromagnetic configurations. The ground state of all superlattices is half-metallic ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: May 23, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.77"> Unearthing the foundational role of anharmonicity in heat transport in glasses</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:wc-yf" class="doi-right">10.24435/materialscloud:wc-yf </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Alfredo Fiorentino, Enrico Drigo, Stefano Baroni, Paolo Pegolo </p> <ul class="list-inline"> <li class="text-justify">The time-honored Allen-Feldman theory of heat transport in glasses is generally assumed to predict a finite value for the thermal conductivity, even if it neglects the anharmonic broadening of vibrational normal modes. We demonstrate that the harmonic approximation predicts that the bulk lattice thermal conductivity of harmonic solids inevitably diverges at any temperature, irrespective of configurational disorder, and that its ability to represent the heat-transport properties observed experimentally in most glasses is implicitly due to finite-size effects. Our theoretical analysis is thoroughly benchmarked against careful numerical simulations. Our findings thus reveal that a proper account of anharmonic effects is indispensable to predict a finite value for the bulk thermal conductivity in any solid material, be it crystalline or glassy. This record contains data and scripts to support the findings of the manuscript and ensure their reproducibility.</li> </ul> <p> <span> Latest version: v1<br> Publication date: May 23, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.76"> The energy landscape of magnetic materials</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:14-b3" class="doi-right">10.24435/materialscloud:14-b3 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Louis Ponet, Enrico Di Lucente, Nicola Marzari </p> <ul class="list-inline"> <li class="text-justify">Magnetic materials can display many solutions to the electronic-structure problem, corresponding to different local or global minima of the energy functional. In Hartree-Fock or density-functional theory different single-determinant solutions lead to different magnetizations, ionic oxidation states, hybridizations, and inter-site magnetic couplings. The vast majority of these states can be fingerprinted through their projection on the atomic orbitals of the magnetic ions. We have devised an approach that provides an effective control over these occupation matrices, allowing us to systematically explore the landscape of the potential energy surface. We showcase the emergence of a complex zoology of self-consistent states; even more so when semi-local density-functional theory is augmented - and typically made more accurate - by Hubbard corrections. Such extensive explorations allow to robustly identify the ground state of magnetic systems, and to assess the accuracy (or not) of current functionals and approximations</li> </ul> <p> <span> Latest version: v1<br> Publication date: May 23, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.75"> Dramatic acceleration of the Hopf cyclization on gold(111): from enediynes to unusual graphene nanoribbons</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:62-ew" class="doi-right">10.24435/materialscloud:62-ew </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Chenxiao Zhao, Carlo A. Pignedoli, Dayanni D. Bhagwandin, Wangwei Xu, Pascal Rufieux, Roman Fasel, Yves Rubin </p> <ul class="list-inline"> <li class="text-justify">Hopf et al. first reported the high-temperature 6π-electrocyclization of cis-hexa-1,3-diene-5-yne to benzene in 1969. Subsequent studies using this cyclization have been limited by its very high reaction barrier. Here, we show that the reaction barrier for two model systems, (E)-1,3,4,6-tetraphenyl-3-hexen-1,5-diyne (1a) and (E)-3,4-bis(4-iodophenyl)-1,6-diphenyl-3-hexen-1,5-diyne 1b, is decreased by nearly half on a Au(111) surface. In recent work, we have used scanning tunneling microscopy (STM) and non-contact atomic force microscopy (nc-AFM) to monitor the Hopf cyclization of enediynes 1a,b on Au(111). Enediyne 1a undergoes two sequential, quantitative Hopf cyclizations, first to naphthalene derivative 2, and finally to chrysene 3. Density functional theory (DFT) calculations reveal that a gold atom from the Au(111) surface is involved in all steps of this reaction, and that it is crucial to lowering the reaction barrier. Our findings have important implications for the ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: May 21, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.74"> Electronic decoupling and hole-doping of graphene nanoribbons on metal substrates by chloride intercalation</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:y5-et" class="doi-right">10.24435/materialscloud:y5-et </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Amogh Kinikar, Thorsten G. Englmann, Marco Di Giovannantonio, Nicolò Bassi, Feifei Xiang, Samuel Stolz, Roland Widmer, Gabriela Borin Barin, Elia Turco, Néstor Merino Díez, Kristjan Eimre, Andres Ortega-Guerrero, Xinliang Feng, Oliver Gröning, Carlo Antonio Pignedoli, Roman Fasel, Pascal Ruffieux </p> <ul class="list-inline"> <li class="text-justify">In this record we provide the data to support our recent finding on the intercalation of gold chloride underneath atomically precise graphene nanoribbons (GNRs). GNRs have a wide range of electronic properties that depend sensitively on their chemical structure. Several types of GNRs have been synthesized on metal surfaces through selective surface-catalyzed reactions. The resulting GNRs are adsorbed on the metal surface, which may lead to hybridization between the GNR orbitals and those of the substrate. This makes investigation of the intrinsic electronic properties of GNRs more difficult, and also rules out capacitive gating. In the manuscript where the data presented here is discussed, we demonstrate the formation of a dielectric gold chloride adlayer that can intercalate underneath GNRs on the Au(111) surface. The intercalated gold chloride adlayer electronically decouples the GNRs from the metal and leads to a substantial hole doping of the GNRs. Our results introduce an ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: May 16, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.73"> FINALES - Electrolyte optimization for maximum conductivity and for maximum cycle life</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:qt-1s" class="doi-right">10.24435/materialscloud:qt-1s </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Simon K. Steensen, Monika Vogler, Francisco Fernando Ramirez, Leon Merker, Jonas Busk, Johan M. Carlsson, Laura Hannemose Rieger, Bojing Zhang, Francois Liot, Giovanni Pizzi, Felix Hanke, Eibar Flores, Hamidreza Hajiyani, Stefan Fuchs, Alexey Sanin, Miran Gaberšček, Ivano E. Castelli, Simon Clark, Tejs Vegge, Arghya Bhowmik, Helge S. Stein </p> <ul class="list-inline"> <li class="text-justify">This study investigates an electrolyte system composed of lithium hexafluorophosphate (LiPF6), ethylene carbonate (EC) and ethyl methyl carbonate (EMC). For the assembly of full cells, electrodes based on graphite and lithium nickel dioxide (LNO) are used. This work provides insight into the similarity of formulations of an electrolyte optimized for maximum conductivity and another one optimized for maximum cycle life are expected to be in this chemical system. The goal is to assess whether it is promising to target research efforts on finding an electrolyte formulation within this chemical space which can fulfill both requirements. A campaign utilizing the latest version of FINALES is designed to determine conductivity values and predict end of life for various electrolyte formulations containing the aforementioned chemicals. The campaigns were able to reproducibly identify regions of high ionic conductivity of the aforementioned chemical composition. The ML methodology applied ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: May 14, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.72"> First-principles thermodynamics of precipitation in aluminum-containing refractory alloys</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:th-d5" class="doi-right">10.24435/materialscloud:th-d5 </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Yann Lorris Müller, Anirudh Raju Natarajan </p> <ul class="list-inline"> <li class="text-justify">Materials for high-temperature environments are actively being investigated for deployment in aerospace and nuclear applications. This study uses computational approaches to unravel the crystallography, and thermodynamics of a promising class of refractory alloys containing aluminum. Accurate first-principles calculations, cluster expansion models, and statistical mechanics techniques are employed to rigorously analyze precipitation in a prototypical senary Al-Nb-Ta-Ti-V-Zr alloy. Finite-temperature calculations reveal a strong tendency for aluminum to segregate to a single sublattice at elevated temperatures. Precipitate and matrix compositions computed with our ab-initio model are in excellent agreement with previous experimental measurements (Soni et al., 2020). Surprisingly, conventional B2-like orderings are found to be both thermodynamically and mechanically unstable in this alloy system. Complex anti-site defects are essential to forming a stable ordered precipitate. Our ...</li> </ul> <p> <span> Latest version: v1<br> Publication date: May 14, 2024 </span> </p> <hr /> <h4 class="record"><a target="_self" href="/record/2024.71"> Seebeck coefficient of ionic conductors from Bayesian regression analysis</a> </h4> <div> <span class="doi-badge"> <span class="doi-left">DOI</span><a href="https://doi.org/10.24435/materialscloud:p1-bm" class="doi-right">10.24435/materialscloud:p1-bm </a> </span> </div> <div class="pt-10"></div> <p class="text-muted"> Enrico Drigo, Stefano Baroni, Paolo Pegolo </p> <ul class="list-inline"> <li class="text-justify">We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the probability distribution of the off-diagonal elements of a Wishart matrix, we develop a consistent and unbiased estimator for the Seebeck coefficient, whose statistical uncertainty can be arbitrarily reduced in the long-time limit. We assess the efficacy of our method by benchmarking it against extensive equilibrium molecular dynamics simulations conducted on molten CsF using empirical force fields. We then employ this procedure to calculate the Seebeck coefficient of molten NaCl, KCl and LiCl using neural network force fields trained on ab initio data over a range of pressure-temperature conditions.</li> </ul> <p> <span> Latest version: v1<br> Publication date: May 13, 2024 </span> </p> <hr /> </div> </div> </div> <div class="col-xs-1"></div> </div> <div class="row pt-10"> <div class="col-xs-1"></div> <div class="col-xs-10 text-right"> Showing: 100 of 917 records </div> <div class="col-xs-1"></div> </div> <div class="row pt-10"> <div class="col-xs-1"></div> <div class="col-xs-10 text-center"> <input type="button" class="btn btn-default" onclick="more()" value="Show more records" /> </div> <div class="col-xs-1"></div> </div> <footer class="footer"> <div class="menu-wrapper"> <div class="container pt-50"> </div> </div> </footer> <script src="/static/dist/js/manifest.542f5ef81bf10bf1ea20.js"></script> <script src="/static/dist/js/1.06385fc1e953c0551c8c.js"></script> <script src="/static/dist/js/3.f7488407095c285dd870.js"></script> <script src="/static/dist/js/4.60556ac1230f88568980.js"></script> <script type="text/javascript"> function more() { const urlParams = new URLSearchParams(window.location.search); var page = urlParams.get('page'); if (page == null || !parseInt(Number(page),10) || page <= 0){ page = 1; } else { page = parseInt(Number(page),10); } page++; location.href = location.origin + location.pathname + '?page=' + page; } jQuery(document).ready(function($){ $(window).scroll(function () { sessionStorage.scrollTop = $(this).scrollTop(); }); if (sessionStorage.scrollTop != "undefined") { $(window).scrollTop(sessionStorage.scrollTop); } }); </script> </body> </html>

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