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</div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.09189">arXiv:2410.09189</a> <span> [<a href="https://arxiv.org/pdf/2410.09189">pdf</a>, <a href="https://arxiv.org/format/2410.09189">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> </div> </div> <p class="title is-5 mathjax"> Simulation of 24,000 Electrons Dynamics: Real-Time Time-Dependent Density Functional Theory (TDDFT) with the Real-Space Multigrids (RMG) </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Jakowski%2C+J">Jacek Jakowski</a>, <a href="/search/physics?searchtype=author&query=Lu%2C+W">Wenchang Lu</a>, <a href="/search/physics?searchtype=author&query=Briggs%2C+E">Emil Briggs</a>, <a href="/search/physics?searchtype=author&query=Lingerfelt%2C+D">David Lingerfelt</a>, <a href="/search/physics?searchtype=author&query=Sumpter%2C+B+G">Bobby G. Sumpter</a>, <a href="/search/physics?searchtype=author&query=Ganesh%2C+P">Panchapakesan Ganesh</a>, <a href="/search/physics?searchtype=author&query=Bernholc%2C+J">Jerzy Bernholc</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.09189v1-abstract-short" style="display: inline;"> We present the theory, implementation, and benchmarking of a real-time time-dependent density functional theory (RT-TDDFT) module within the RMG code, designed to simulate the electronic response of molecular systems to external perturbations. Our method offers insights into non-equilibrium dynamics and excited states across a diverse range of systems, from small organic molecules to large metalli… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.09189v1-abstract-full').style.display = 'inline'; document.getElementById('2410.09189v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.09189v1-abstract-full" style="display: none;"> We present the theory, implementation, and benchmarking of a real-time time-dependent density functional theory (RT-TDDFT) module within the RMG code, designed to simulate the electronic response of molecular systems to external perturbations. Our method offers insights into non-equilibrium dynamics and excited states across a diverse range of systems, from small organic molecules to large metallic nanoparticles. Benchmarking results demonstrate excellent agreement with established TDDFT implementations and showcase the superior stability of our time-integration algorithm, enabling long-term simulations with minimal energy drift. The scalability and efficiency of RMG on massively parallel architectures allow for simulations of complex systems, such as plasmonic nanoparticles with thousands of atoms. Future extensions, including nuclear and spin dynamics, will broaden the applicability of this RT-TDDFT implementation, providing a powerful toolset for studies of photoactive materials, nanoscale devices, and other systems where real-time electronic dynamics is essential. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.09189v1-abstract-full').style.display = 'none'; document.getElementById('2410.09189v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.05574">arXiv:2410.05574</a> <span> [<a href="https://arxiv.org/pdf/2410.05574">pdf</a>, <a href="https://arxiv.org/format/2410.05574">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Soft Condensed Matter">cond-mat.soft</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> </div> <p class="title is-5 mathjax"> Machine Learning Inversion from Scattering for Mechanically Driven Polymers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Ding%2C+L">Lijie Ding</a>, <a href="/search/physics?searchtype=author&query=Tung%2C+C">Chi-Huan Tung</a>, <a href="/search/physics?searchtype=author&query=Sumpter%2C+B+G">Bobby G. Sumpter</a>, <a href="/search/physics?searchtype=author&query=Chen%2C+W">Wei-Ren Chen</a>, <a href="/search/physics?searchtype=author&query=Do%2C+C">Changwoo Do</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.05574v1-abstract-short" style="display: inline;"> We develop a Machine Learning Inversion method for analyzing scattering functions of mechanically driven polymers and extracting the corresponding feature parameters, which include energy parameters and conformation variables. The polymer is modeled as a chain of fixed-length bonds constrained by bending energy, and it is subject to external forces such as stretching and shear. We generate a data… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.05574v1-abstract-full').style.display = 'inline'; document.getElementById('2410.05574v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.05574v1-abstract-full" style="display: none;"> We develop a Machine Learning Inversion method for analyzing scattering functions of mechanically driven polymers and extracting the corresponding feature parameters, which include energy parameters and conformation variables. The polymer is modeled as a chain of fixed-length bonds constrained by bending energy, and it is subject to external forces such as stretching and shear. We generate a data set consisting of random combinations of energy parameters, including bending modulus, stretching, and shear force, along with Monte Carlo-calculated scattering functions and conformation variables such as end-to-end distance, radius of gyration, and the off-diagonal component of the gyration tensor. The effects of the energy parameters on the polymer are captured by the scattering function, and principal component analysis ensures the feasibility of the Machine Learning inversion. Finally, we train a Gaussian Process Regressor using part of the data set as a training set and validate the trained regressor for inversion using the rest of the data. The regressor successfully extracts the feature parameters. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.05574v1-abstract-full').style.display = 'none'; document.getElementById('2410.05574v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 7 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.08226">arXiv:2308.08226</a> <span> [<a href="https://arxiv.org/pdf/2308.08226">pdf</a>, <a href="https://arxiv.org/format/2308.08226">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Soft Condensed Matter">cond-mat.soft</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</span> </div> </div> <p class="title is-5 mathjax"> Accelerated Design of Block Copolymers: An Unbiased Exploration Strategy via Fusion of Molecular Dynamics Simulations and Machine Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Carrillo%2C+J+M+Y">Jan Michael Y. Carrillo</a>, <a href="/search/physics?searchtype=author&query=P%2C+V">Vijith P</a>, <a href="/search/physics?searchtype=author&query=Patra%2C+T+K">Tarak K. Patra</a>, <a href="/search/physics?searchtype=author&query=Chen%2C+Z">Zhan Chen</a>, <a href="/search/physics?searchtype=author&query=Russell%2C+T+P">Thomas P. Russell</a>, <a href="/search/physics?searchtype=author&query=Sankaranarayanan%2C+S+K">Subramanian KRS Sankaranarayanan</a>, <a href="/search/physics?searchtype=author&query=Sumpter%2C+B+G">Bobby G. Sumpter</a>, <a href="/search/physics?searchtype=author&query=Batra%2C+R">Rohit Batra</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2308.08226v1-abstract-short" style="display: inline;"> Star block copolymers (s-BCPs) have potential applications as novel surfactants or amphiphiles for emulsification, compatbilization, chemical transformations and separations. s-BCPs are star-shaped macromolecules comprised of linear chains of different chemical blocks (e.g., solvophilic and solvophobic blocks) that are covalently joined at one junction point. Various parameters of these macromolec… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.08226v1-abstract-full').style.display = 'inline'; document.getElementById('2308.08226v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.08226v1-abstract-full" style="display: none;"> Star block copolymers (s-BCPs) have potential applications as novel surfactants or amphiphiles for emulsification, compatbilization, chemical transformations and separations. s-BCPs are star-shaped macromolecules comprised of linear chains of different chemical blocks (e.g., solvophilic and solvophobic blocks) that are covalently joined at one junction point. Various parameters of these macromolecules can be tuned to obtain desired surface properties, including the number of arms, composition of the arms, and the degree-of-polymerization of the blocks (or the length of the arm). This makes identification of the optimal s-BCP design highly non-trivial as the total number of plausible s-BCPs architectures is experimentally or computationally intractable. In this work, we use molecular dynamics (MD) simulations coupled with reinforcement learning based Monte Carlo tree search (MCTS) to identify s-BCPs designs that minimize the interfacial tension between polar and non-polar solvents. We first validate the MCTS approach for design of small- and medium-sized s-BCPs, and then use it to efficiently identify sequences of copolymer blocks for large-sized s-BCPs. The structural origins of interfacial tension in these systems are also identified using the configurations obtained from MD simulations. Chemical insights on the arrangement of copolymer blocks that promote lower interfacial tension were mined using machine learning (ML) techniques. Overall, this work provides an efficient approach to solve design problems via fusion of simulations and ML and provide important groundwork for future experimental investigation of s-BCPs sequences for various applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.08226v1-abstract-full').style.display = 'none'; document.getElementById('2308.08226v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.11189">arXiv:2307.11189</a> <span> [<a href="https://arxiv.org/pdf/2307.11189">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</span> </div> </div> <p class="title is-5 mathjax"> Nanoscale imaging of He-ion irradiation effects on amorphous TaO$_x$ toward electroforming-free neuromorphic functions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Popova%2C+O">Olha Popova</a>, <a href="/search/physics?searchtype=author&query=Randolph%2C+S+J">Steven J. Randolph</a>, <a href="/search/physics?searchtype=author&query=Neumayer%2C+S+M">Sabine M. Neumayer</a>, <a href="/search/physics?searchtype=author&query=Liang%2C+L">Liangbo Liang</a>, <a href="/search/physics?searchtype=author&query=Lawrie%2C+B">Benjamin Lawrie</a>, <a href="/search/physics?searchtype=author&query=Ovchinnikova%2C+O+S">Olga S. Ovchinnikova</a>, <a href="/search/physics?searchtype=author&query=Bondi%2C+R+J">Robert J. Bondi</a>, <a href="/search/physics?searchtype=author&query=Marinella%2C+M+J">Matthew J. Marinella</a>, <a href="/search/physics?searchtype=author&query=Sumpter%2C+B+G">Bobby G. Sumpter</a>, <a href="/search/physics?searchtype=author&query=Maksymovych%2C+P">Petro Maksymovych</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.11189v1-abstract-short" style="display: inline;"> Resistive switching in thin films has been widely studied in a broad range of materials. Yet the mechanisms behind electroresistive switching have been persistently difficult to decipher and control, in part due to their non-equilibrium nature. Here, we demonstrate new experimental approaches that can probe resistive switching phenomena, utilizing amorphous TaO$_x$ as a model material system. Spec… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.11189v1-abstract-full').style.display = 'inline'; document.getElementById('2307.11189v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.11189v1-abstract-full" style="display: none;"> Resistive switching in thin films has been widely studied in a broad range of materials. Yet the mechanisms behind electroresistive switching have been persistently difficult to decipher and control, in part due to their non-equilibrium nature. Here, we demonstrate new experimental approaches that can probe resistive switching phenomena, utilizing amorphous TaO$_x$ as a model material system. Specifically, we apply Scanning Microwave Impedance Microscopy (sMIM) and cathodoluminescence (CL) microscopy as direct probes of conductance and electronic structure, respectively. These methods provide direct evidence of the electronic state of TaO$_x$ despite its amorphous nature. For example CL identifies characteristic impurity levels in TaO$_x$, in agreement with first principles calculations. We applied these methods to investigate He-ion-beam irradiation as a path to activate conductivity of materials and enable electroforming-free control over resistive switching. However, we find that even though He-ions begin to modify the nature of bonds even at the lowest doses, the films conductive properties exhibit remarkable stability with large displacement damage and they are driven to metallic states only at the limit of structural decomposition. Finally, we show that electroforming in a nanoscale junction can be carried out with a dissipated power of < 20 nW, a much smaller value compared to earlier studies and one that minimizes irreversible structural modifications of the films. The multimodal approach described here provides a new framework toward the theory/experiment guided design and optimization of electroresistive materials. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.11189v1-abstract-full').style.display = 'none'; document.getElementById('2307.11189v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2204.05095">arXiv:2204.05095</a> <span> [<a href="https://arxiv.org/pdf/2204.05095">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> </div> </div> <p class="title is-5 mathjax"> Physics is the New Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Kalinin%2C+S+V">Sergei V. Kalinin</a>, <a href="/search/physics?searchtype=author&query=Ziatdinov%2C+M">Maxim Ziatdinov</a>, <a href="/search/physics?searchtype=author&query=Sumpter%2C+B+G">Bobby G. Sumpter</a>, <a href="/search/physics?searchtype=author&query=White%2C+A+D">Andrew D. White</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2204.05095v1-abstract-short" style="display: inline;"> The rapid development of machine learning (ML) methods has fundamentally affected numerous applications ranging from computer vision, biology, and medicine to accounting and text analytics. Until now, it was the availability of large and often labeled data sets that enabled significant breakthroughs. However, the adoption of these methods in classical physical disciplines has been relatively slow,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.05095v1-abstract-full').style.display = 'inline'; document.getElementById('2204.05095v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2204.05095v1-abstract-full" style="display: none;"> The rapid development of machine learning (ML) methods has fundamentally affected numerous applications ranging from computer vision, biology, and medicine to accounting and text analytics. Until now, it was the availability of large and often labeled data sets that enabled significant breakthroughs. However, the adoption of these methods in classical physical disciplines has been relatively slow, a tendency that can be traced to the intrinsic differences between correlative approaches of purely data-based ML and the causal hypothesis-driven nature of physical sciences. Furthermore, anomalous behaviors of classical ML necessitate addressing issues such as explainability and fairness of ML. We also note the sequence in which deep learning became mainstream in different scientific disciplines - starting from medicine and biology and then towards theoretical chemistry, and only after that, physics - is rooted in the progressively more complex level of descriptors, constraints, and causal structures available for incorporation in ML architectures. Here we put forth that over the next decade, physics will become a new data, and this will continue the transition from dot-coms and scientific computing concepts of the 90ies to big data of 2000-2010 to deep learning of 2010-2020 to physics-enabled scientific ML. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2204.05095v1-abstract-full').style.display = 'none'; document.getElementById('2204.05095v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 April, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2101.08449">arXiv:2101.08449</a> <span> [<a href="https://arxiv.org/pdf/2101.08449">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Ghosh%2C+A">Ayana Ghosh</a>, <a href="/search/physics?searchtype=author&query=Sumpter%2C+B+G">Bobby G. Sumpter</a>, <a href="/search/physics?searchtype=author&query=Dyck%2C+O">Ondrej Dyck</a>, <a href="/search/physics?searchtype=author&query=Kalinin%2C+S+V">Sergei V. Kalinin</a>, <a href="/search/physics?searchtype=author&query=Ziatdinov%2C+M">Maxim Ziatdinov</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2101.08449v2-abstract-short" style="display: inline;"> Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest. However, applications of deep learning in experimental domains are often limited by the out-of-distribution drift between the experiments, where the network trained for one set of imagi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.08449v2-abstract-full').style.display = 'inline'; document.getElementById('2101.08449v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2101.08449v2-abstract-full" style="display: none;"> Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest. However, applications of deep learning in experimental domains are often limited by the out-of-distribution drift between the experiments, where the network trained for one set of imaging conditions becomes sub-optimal for different ones. This limitation is particularly stringent in the quest to have an automated experiment setting, where retraining or transfer learning becomes impractical due to the need for human intervention and associated latencies. Here we explore the reproducibility of deep learning for feature extraction in atom-resolved electron microscopy and introduce workflows based on ensemble learning and iterative training to greatly improve feature detection. This approach both allows incorporating uncertainty quantification into the deep learning analysis and also enables rapid automated experimental workflows where retraining of the network to compensate for out-of-distribution drift due to subtle change in imaging conditions is substituted for a human operator or programmatic selection of networks from the ensemble. This methodology can be further applied to machine learning workflows in other imaging areas including optical and chemical imaging. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.08449v2-abstract-full').style.display = 'none'; document.getElementById('2101.08449v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Add supplemental material</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.01831">arXiv:2007.01831</a> <span> [<a href="https://arxiv.org/pdf/2007.01831">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s41524-020-00440-1">10.1038/s41524-020-00440-1 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Choudhary%2C+K">Kamal Choudhary</a>, <a href="/search/physics?searchtype=author&query=Garrity%2C+K+F">Kevin F. Garrity</a>, <a href="/search/physics?searchtype=author&query=Reid%2C+A+C+E">Andrew C. E. Reid</a>, <a href="/search/physics?searchtype=author&query=DeCost%2C+B">Brian DeCost</a>, <a href="/search/physics?searchtype=author&query=Biacchi%2C+A+J">Adam J. Biacchi</a>, <a href="/search/physics?searchtype=author&query=Walker%2C+A+R+H">Angela R. Hight Walker</a>, <a href="/search/physics?searchtype=author&query=Trautt%2C+Z">Zachary Trautt</a>, <a href="/search/physics?searchtype=author&query=Hattrick-Simpers%2C+J">Jason Hattrick-Simpers</a>, <a href="/search/physics?searchtype=author&query=Kusne%2C+A+G">A. Gilad Kusne</a>, <a href="/search/physics?searchtype=author&query=Centrone%2C+A">Andrea Centrone</a>, <a href="/search/physics?searchtype=author&query=Davydov%2C+A">Albert Davydov</a>, <a href="/search/physics?searchtype=author&query=Jiang%2C+J">Jie Jiang</a>, <a href="/search/physics?searchtype=author&query=Pachter%2C+R">Ruth Pachter</a>, <a href="/search/physics?searchtype=author&query=Cheon%2C+G">Gowoon Cheon</a>, <a href="/search/physics?searchtype=author&query=Reed%2C+E">Evan Reed</a>, <a href="/search/physics?searchtype=author&query=Agrawal%2C+A">Ankit Agrawal</a>, <a href="/search/physics?searchtype=author&query=Qian%2C+X">Xiaofeng Qian</a>, <a href="/search/physics?searchtype=author&query=Sharma%2C+V">Vinit Sharma</a>, <a href="/search/physics?searchtype=author&query=Zhuang%2C+H">Houlong Zhuang</a>, <a href="/search/physics?searchtype=author&query=Kalinin%2C+S+V">Sergei V. Kalinin</a>, <a href="/search/physics?searchtype=author&query=Sumpter%2C+B+G">Bobby G. Sumpter</a>, <a href="/search/physics?searchtype=author&query=Pilania%2C+G">Ghanshyam Pilania</a>, <a href="/search/physics?searchtype=author&query=Acar%2C+P">Pinar Acar</a>, <a href="/search/physics?searchtype=author&query=Mandal%2C+S">Subhasish Mandal</a>, <a href="/search/physics?searchtype=author&query=Haule%2C+K">Kristjan Haule</a> , et al. (3 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2007.01831v2-abstract-short" style="display: inline;"> The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.01831v2-abstract-full').style.display = 'inline'; document.getElementById('2007.01831v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.01831v2-abstract-full" style="display: none;"> The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-Tools. To date, JARVIS consists of 40,000 materials and 1 million calculated properties in JARVIS-DFT, 1,500 materials and 110 force-fields in JARVIS-FF, and 25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-Tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov . <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.01831v2-abstract-full').style.display = 'none'; document.getElementById('2007.01831v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2002.12193">arXiv:2002.12193</a> <span> [<a href="https://arxiv.org/pdf/2002.12193">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> </div> <p class="title is-5 mathjax"> Reconstruction of effective potential from statistical analysis of dynamic trajectories </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Nobakht%2C+A+Y">Ali Yousefzadi Nobakht</a>, <a href="/search/physics?searchtype=author&query=Dyck%2C+O">Ondrej Dyck</a>, <a href="/search/physics?searchtype=author&query=Lingerfelt%2C+D+B">David B. Lingerfelt</a>, <a href="/search/physics?searchtype=author&query=Bao%2C+F">Feng Bao</a>, <a href="/search/physics?searchtype=author&query=Ziatdinov%2C+M">Maxim Ziatdinov</a>, <a href="/search/physics?searchtype=author&query=Maksov%2C+A">Artem Maksov</a>, <a href="/search/physics?searchtype=author&query=Sumpter%2C+B+G">Bobby G. Sumpter</a>, <a href="/search/physics?searchtype=author&query=Archibald%2C+R">Richard Archibald</a>, <a href="/search/physics?searchtype=author&query=Jesse%2C+S">Stephen Jesse</a>, <a href="/search/physics?searchtype=author&query=Kalinin%2C+S+V">Sergei V. Kalinin</a>, <a href="/search/physics?searchtype=author&query=Law%2C+K+J+H">Kody J. H. Law</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2002.12193v1-abstract-short" style="display: inline;"> The broad incorporation of microscopic methods is yielding a wealth of information on atomic and mesoscale dynamics of individual atoms, molecules, and particles on surfaces and in open volumes. Analysis of such data necessitates statistical frameworks to convert observed dynamic behaviors to effective properties of materials. Here we develop a method for stochastic reconstruction of effective act… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.12193v1-abstract-full').style.display = 'inline'; document.getElementById('2002.12193v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2002.12193v1-abstract-full" style="display: none;"> The broad incorporation of microscopic methods is yielding a wealth of information on atomic and mesoscale dynamics of individual atoms, molecules, and particles on surfaces and in open volumes. Analysis of such data necessitates statistical frameworks to convert observed dynamic behaviors to effective properties of materials. Here we develop a method for stochastic reconstruction of effective acting potentials from observed trajectories. Using the Silicon vacancy defect in graphene as a model, we develop a statistical framework to reconstruct the free energy landscape from calculated atomic displacements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2002.12193v1-abstract-full').style.display = 'none'; document.getElementById('2002.12193v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 February, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 5 figures. This manuscript is a part of update to previous work (arXiv:1804.03729v1) authors found some of the analysis in the previous work to be not accurate and this manuscript is a partial update to that work</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1806.05169">arXiv:1806.05169</a> <span> [<a href="https://arxiv.org/pdf/1806.05169">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</span> </div> </div> <p class="title is-5 mathjax"> Probing static discharge of polymer surfaces with nanoscale resolution </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Borodinov%2C+N">Nikolay Borodinov</a>, <a href="/search/physics?searchtype=author&query=Ievlev%2C+A+V">Anton V. Ievlev</a>, <a href="/search/physics?searchtype=author&query=Carrillo%2C+J">Jan-Michael Carrillo</a>, <a href="/search/physics?searchtype=author&query=Calamari%2C+A">Andrea Calamari</a>, <a href="/search/physics?searchtype=author&query=Mamak%2C+M">Marc Mamak</a>, <a href="/search/physics?searchtype=author&query=Mulcahy%2C+J">John Mulcahy</a>, <a href="/search/physics?searchtype=author&query=Sumpter%2C+B+G">Bobby G. Sumpter</a>, <a href="/search/physics?searchtype=author&query=Ovchinnikova%2C+O+S">Olga S. Ovchinnikova</a>, <a href="/search/physics?searchtype=author&query=Maksymovych%2C+P">Petro Maksymovych</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1806.05169v1-abstract-short" style="display: inline;"> Triboelectric charging strongly affects the operation cycle and handling of materials and can be used to harvest mechanical energy through triboelectric nanogenerator set-up. Despite ubiquity of triboelectric effects, a lot of mechanisms surrounding the relevant phenomena remain to be understood. Continued progress will rely on the development of rapid and reliable methods to probe accumulation an… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1806.05169v1-abstract-full').style.display = 'inline'; document.getElementById('1806.05169v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1806.05169v1-abstract-full" style="display: none;"> Triboelectric charging strongly affects the operation cycle and handling of materials and can be used to harvest mechanical energy through triboelectric nanogenerator set-up. Despite ubiquity of triboelectric effects, a lot of mechanisms surrounding the relevant phenomena remain to be understood. Continued progress will rely on the development of rapid and reliable methods to probe accumulation and dynamics of static charges. Here, we demonstrate in-situ quantification of tribological charging with nanoscale resolution, that is applicable to a wide range of dielectric systems. We apply this method to differentiate between strongly and weakly charging compositions of industrial grade polymers. The method highlights the complex phenomena of electrostatic discharge upon contact formation to pre-charged surfaces, and directly reveals the mobility of electrostatic charge on the surface. Systematic characterization of commercial polyethylene terephthalate samples revealed the compositions with the best antistatic properties and provided an estimate of characteristic charge density up to 5x10-5 C/m2. Large-scale molecular dynamics simulations were used to resolve atomistic level structural and dynamical details revealing enrichment of oxygen containing groups near the air-interface where electrostatic charges are likely to accumulate. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1806.05169v1-abstract-full').style.display = 'none'; document.getElementById('1806.05169v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 June, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2018. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1708.02614">arXiv:1708.02614</a> <span> [<a href="https://arxiv.org/pdf/1708.02614">pdf</a>, <a href="https://arxiv.org/format/1708.02614">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Mesoscale and Nanoscale Physics">cond-mat.mes-hall</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1039/C7NR05839J">10.1039/C7NR05839J <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Interlayer bond polarizability model for stacking-dependent low-frequency Raman scattering in layered materials </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Liang%2C+L">Liangbo Liang</a>, <a href="/search/physics?searchtype=author&query=Puretzky%2C+A+A">Alexander A. Puretzky</a>, <a href="/search/physics?searchtype=author&query=Sumpter%2C+B+G">Bobby G. Sumpter</a>, <a href="/search/physics?searchtype=author&query=Meunier%2C+V">Vincent Meunier</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1708.02614v1-abstract-short" style="display: inline;"> Two-dimensional (2D) layered materials have been extensively studied owing to their fascinating and technologically relevant properties. Their functionalities can be often tailored by the interlayer stacking pattern. Low-frequency (LF) Raman spectroscopy provides a quick, non-destructive and inexpensive optical technique for stacking characterization, since the intensities of LF interlayer vibrati… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1708.02614v1-abstract-full').style.display = 'inline'; document.getElementById('1708.02614v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1708.02614v1-abstract-full" style="display: none;"> Two-dimensional (2D) layered materials have been extensively studied owing to their fascinating and technologically relevant properties. Their functionalities can be often tailored by the interlayer stacking pattern. Low-frequency (LF) Raman spectroscopy provides a quick, non-destructive and inexpensive optical technique for stacking characterization, since the intensities of LF interlayer vibrational modes are sensitive to the details of the stacking. A simple and generalized interlayer bond polarizability model is proposed here to explain and predict how the LF Raman intensities depend on complex stacking sequences for any thickness in a broad array of 2D materials, including graphene, MoS2, MoSe2, NbSe2, Bi2Se3, GaSe, h-BN, etc. Additionally, a general strategy is proposed to unify the stacking nomenclature for these 2D materials. Our model reveals the fundamental mechanism of LF Raman response to the stacking, and provides general rules for stacking identification. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1708.02614v1-abstract-full').style.display = 'none'; document.getElementById('1708.02614v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 August, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">34 pages, 7 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Report number:</span> 2017 Nanoscale HOT Article Collection </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Nanoscale, 2017 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1509.00688">arXiv:1509.00688</a> <span> [<a href="https://arxiv.org/pdf/1509.00688">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Soft Condensed Matter">cond-mat.soft</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1063/1.4935595">10.1063/1.4935595 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Revealing spatially heterogeneous relaxation in a model nanocomposite </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Cheng%2C+S">Shiwang Cheng</a>, <a href="/search/physics?searchtype=author&query=Mirigian%2C+S">Stephen Mirigian</a>, <a href="/search/physics?searchtype=author&query=Carrillo%2C+J+Y">Jan-Michael Y. Carrillo</a>, <a href="/search/physics?searchtype=author&query=Bocharova%2C+V">Vera Bocharova</a>, <a href="/search/physics?searchtype=author&query=Sumpter%2C+B+G">Bobby G. Sumpter</a>, <a href="/search/physics?searchtype=author&query=Schweizer%2C+K+S">Kenneth S. Schweizer</a>, <a href="/search/physics?searchtype=author&query=Sokolov%2C+A+P">Alexei P. Sokolov</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1509.00688v1-abstract-short" style="display: inline;"> The detailed nature of spatially heterogeneous dynamics of glycerol-silica nanocomposites is unraveled by combining dielectric spectroscopy with atomistic simulation and statistical mechanical theory. Analysis of the spatial mobility gradient shows no 'glassy' layer, but the alpha relaxation time near the nanoparticle grows with cooling faster than the alpha relaxation time in the bulk, and is ~ 2… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1509.00688v1-abstract-full').style.display = 'inline'; document.getElementById('1509.00688v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1509.00688v1-abstract-full" style="display: none;"> The detailed nature of spatially heterogeneous dynamics of glycerol-silica nanocomposites is unraveled by combining dielectric spectroscopy with atomistic simulation and statistical mechanical theory. Analysis of the spatial mobility gradient shows no 'glassy' layer, but the alpha relaxation time near the nanoparticle grows with cooling faster than the alpha relaxation time in the bulk, and is ~ 20 times longer at low temperatures. The interfacial layer thickness increases from ~ 1.8 nm at higher temperatures to ~ 3.5 nm upon cooling to near Tg. A real space microscopic description of the mobility gradient is constructed by synergistically combining high temperature atomistic simulation with theory. Our analysis suggests that the interfacial slowing down arises mainly due to an increase of the local cage scale barrier for activated hopping induced by enhanced packing and densification near the nanoparticle surface. The theory is employed to predict how local surface densification can be manipulated to control layer dynamics and shear rigidity over a wide temperature range. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1509.00688v1-abstract-full').style.display = 'none'; document.getElementById('1509.00688v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 September, 2015; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2015. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">To be submitted to The Journal of Chemical Physics</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/0706.0863">arXiv:0706.0863</a> <span> [<a href="https://arxiv.org/pdf/0706.0863">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1063/1.2770722">10.1063/1.2770722 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> First principles study of magnetism in nanographenes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Jiang%2C+D">De-en Jiang</a>, <a href="/search/physics?searchtype=author&query=Sumpter%2C+B+G">Bobby G. Sumpter</a>, <a href="/search/physics?searchtype=author&query=Dai%2C+S">Sheng Dai</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="0706.0863v2-abstract-short" style="display: inline;"> Magnetism in nanographenes (also know as polycyclic aromatic hydrocarbons, or PAHs) are studied with first principles density functional calculations. We find that an antiferromagnetic (AFM) phase appears as the PAH reaches a certain size. This AFM phase in PAHs has the same origin as the one in infinitely long zigzag-edged graphene nanoribbons, namely, from the localized electronic state at the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0706.0863v2-abstract-full').style.display = 'inline'; document.getElementById('0706.0863v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="0706.0863v2-abstract-full" style="display: none;"> Magnetism in nanographenes (also know as polycyclic aromatic hydrocarbons, or PAHs) are studied with first principles density functional calculations. We find that an antiferromagnetic (AFM) phase appears as the PAH reaches a certain size. This AFM phase in PAHs has the same origin as the one in infinitely long zigzag-edged graphene nanoribbons, namely, from the localized electronic state at the zigzag edge. The smallest PAH still having an AFM ground state is identified. With increased length of the zigzag edge, PAHs approach an infinitely long ribbon in terms of (1) the energetic ordering and difference among the AFM, ferromagnetic (FM), and nonmagnetic (NM) phases and (2) the average local magnetic moment at the zigzag edges. These PAHs serve as ideal targets for chemical synthesis of nanographenes that possess magnetic properties. Moreover, our calculations support the interpretation that experimentally observed magnetism in activated carbon fibers originates from the zigzag edges of the nanographenes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0706.0863v2-abstract-full').style.display = 'none'; document.getElementById('0706.0863v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 July, 2007; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 June, 2007; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2007. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages, 4 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> J. Chem. Phys. 127, 124703 (2007) (5 pages) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/physics/0702209">arXiv:physics/0702209</a> <span> [<a href="https://arxiv.org/pdf/physics/0702209">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Chemical Physics">physics.chem-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Physics">physics.comp-ph</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1063/1.2715558">10.1063/1.2715558 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> The unique chemical reactivity of a graphene nanoribbon's zigzag edge </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Jiang%2C+D">De-en Jiang</a>, <a href="/search/physics?searchtype=author&query=Sumpter%2C+B+G">Bobby G. Sumpter</a>, <a href="/search/physics?searchtype=author&query=Dai%2C+S">Sheng Dai</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="physics/0702209v1-abstract-short" style="display: inline;"> The zigzag edge of a graphene nanoribbon possesses a unique electronic state that is near the Fermi level and localized at the edge carbon atoms. We investigate the chemical reactivity of these zigzag edge sites by examining their reaction energetics with common radicals from first principles. A "partial radical" concept for the edge carbon atoms is introduced to characterize their chemical reac… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('physics/0702209v1-abstract-full').style.display = 'inline'; document.getElementById('physics/0702209v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="physics/0702209v1-abstract-full" style="display: none;"> The zigzag edge of a graphene nanoribbon possesses a unique electronic state that is near the Fermi level and localized at the edge carbon atoms. We investigate the chemical reactivity of these zigzag edge sites by examining their reaction energetics with common radicals from first principles. A "partial radical" concept for the edge carbon atoms is introduced to characterize their chemical reactivity, and the validity of this concept is verified by comparing the dissociation energies of edge-radical bonds with similar bonds in molecules. In addition, the uniqueness of the zigzag-edged graphene nanoribbon is further demonstrated by comparing it with other forms of sp2 carbons, including a graphene sheet, nanotubes, and an armchair-edged graphene nanoribbon. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('physics/0702209v1-abstract-full').style.display = 'none'; document.getElementById('physics/0702209v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 February, 2007; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2007. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">24 pages, 9 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> J. Chem. Phys. 126, 134701 (2007) (6 pages) </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> <a href="https://info.arxiv.org/help/contact.html"> Contact</a> </li> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>subscribe to arXiv mailings</title><desc>Click here to subscribe</desc><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> <a href="https://info.arxiv.org/help/subscribe"> Subscribe</a> </li> </ul> </div> </div> </div>   <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/license/index.html">Copyright</a></li> <li><a href="https://info.arxiv.org/help/policies/privacy_policy.html">Privacy Policy</a></li> </ul> </div> <div class="column sorry-app-links"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/help/web_accessibility.html">Web Accessibility Assistance</a></li> <li> <p class="help"> <a class="a11y-main-link" href="https://status.arxiv.org" target="_blank">arXiv Operational Status <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 256 512" class="icon filter-dark_grey" role="presentation"><path d="M224.3 273l-136 136c-9.4 9.4-24.6 9.4-33.9 0l-22.6-22.6c-9.4-9.4-9.4-24.6 0-33.9l96.4-96.4-96.4-96.4c-9.4-9.4-9.4-24.6 0-33.9L54.3 103c9.4-9.4 24.6-9.4 33.9 0l136 136c9.5 9.4 9.5 24.6.1 34z"/></svg></a><br> Get status notifications via <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/email/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg>email</a> or <a class="is-link" href="https://subscribe.sorryapp.com/24846f03/slack/new" target="_blank"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512" class="icon filter-black" role="presentation"><path d="M94.12 315.1c0 25.9-21.16 47.06-47.06 47.06S0 341 0 315.1c0-25.9 21.16-47.06 47.06-47.06h47.06v47.06zm23.72 0c0-25.9 21.16-47.06 47.06-47.06s47.06 21.16 47.06 47.06v117.84c0 25.9-21.16 47.06-47.06 47.06s-47.06-21.16-47.06-47.06V315.1zm47.06-188.98c-25.9 0-47.06-21.16-47.06-47.06S139 32 164.9 32s47.06 21.16 47.06 47.06v47.06H164.9zm0 23.72c25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06H47.06C21.16 243.96 0 222.8 0 196.9s21.16-47.06 47.06-47.06H164.9zm188.98 47.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06s-21.16 47.06-47.06 47.06h-47.06V196.9zm-23.72 0c0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06V79.06c0-25.9 21.16-47.06 47.06-47.06 25.9 0 47.06 21.16 47.06 47.06V196.9zM283.1 385.88c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06-25.9 0-47.06-21.16-47.06-47.06v-47.06h47.06zm0-23.72c-25.9 0-47.06-21.16-47.06-47.06 0-25.9 21.16-47.06 47.06-47.06h117.84c25.9 0 47.06 21.16 47.06 47.06 0 25.9-21.16 47.06-47.06 47.06H283.1z"/></svg>slack</a> </p> </li> </ul> </div> </div> </div>  </div> </footer> <script src="https://static.arxiv.org/static/base/1.0.0a5/js/member_acknowledgement.js"></script> </body> </html>

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