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<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/2406.13824">arXiv:2406.13824</a> <span> [<a href="https://arxiv.org/pdf/2406.13824">pdf</a>, <a href="https://arxiv.org/format/2406.13824">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Symmetrically Fair Allocations of Indivisible Goods </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Johnston%2C+C">Connor Johnston</a>, <a href="/search/cs?searchtype=author&query=Kazachkov%2C+A+M">Aleksandr M. Kazachkov</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="2406.13824v1-abstract-short" style="display: inline;"> We consider allocating indivisible goods with provable fairness guarantees that are satisfied regardless of which bundle of items each agent receives. Symmetrical allocations of this type are known to exist for divisible resources, such as consensus splitting of a cake into parts, each having equal value for all agents, ensuring that in any allocation of the cake slices, no agent would envy anothe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.13824v1-abstract-full').style.display = 'inline'; document.getElementById('2406.13824v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.13824v1-abstract-full" style="display: none;"> We consider allocating indivisible goods with provable fairness guarantees that are satisfied regardless of which bundle of items each agent receives. Symmetrical allocations of this type are known to exist for divisible resources, such as consensus splitting of a cake into parts, each having equal value for all agents, ensuring that in any allocation of the cake slices, no agent would envy another. For indivisible goods, one analogous concept relaxes envy freeness to guarantee the existence of an allocation in which any bundle is worth as much as any other, up to the value of a bounded number of items from the other bundle. Previous work has studied the number of items that need to be removed. In this paper, we improve upon these bounds for the specific setting in which the number of bundles equals the number of agents. Concretely, we develop the theory of symmetrically envy free up to one good, or symEF1, allocations. We prove that a symEF1 allocation exists if the vertices of a related graph can be partitioned (colored) into as many independent sets as there are agents. This sufficient condition always holds for two agents, and for agents that have identical, disjoint, or binary valuations. We further prove conditions under which exponentially-many distinct symEF1 allocations exist. Finally, we perform computational experiments to study the incidence of symEF1 allocations as a function of the number of agents and items when valuations are drawn uniformly at random. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.13824v1-abstract-full').style.display = 'none'; document.getElementById('2406.13824v1-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> 19 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.01004">arXiv:2403.01004</a> <span> [<a href="https://arxiv.org/pdf/2403.01004">pdf</a>, <a href="https://arxiv.org/format/2403.01004">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Astrophysics of Galaxies">astro-ph.GA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Solar and Stellar Astrophysics">astro-ph.SR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Numerical Analysis">math.NA</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"> Advancing parabolic operators in thermodynamic MHD models II: Evaluating a Practical Time Step Limit for Unconditionally Stable Methods </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Caplan%2C+R+M">Ronald M. Caplan</a>, <a href="/search/cs?searchtype=author&query=Johnston%2C+C+D">Craig D. Johnston</a>, <a href="/search/cs?searchtype=author&query=Daldoff%2C+L+K+S">Lars K. S. Daldoff</a>, <a href="/search/cs?searchtype=author&query=Linker%2C+J+A">Jon A. Linker</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="2403.01004v1-abstract-short" style="display: inline;"> Unconditionally stable time stepping schemes are useful and often practically necessary for advancing parabolic operators in multi-scale systems. However, serious accuracy problems may emerge when taking time steps that far exceed the explicit stability limits. In our previous work, we compared the accuracy and performance of advancing parabolic operators in a thermodynamic MHD model using an impl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.01004v1-abstract-full').style.display = 'inline'; document.getElementById('2403.01004v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.01004v1-abstract-full" style="display: none;"> Unconditionally stable time stepping schemes are useful and often practically necessary for advancing parabolic operators in multi-scale systems. However, serious accuracy problems may emerge when taking time steps that far exceed the explicit stability limits. In our previous work, we compared the accuracy and performance of advancing parabolic operators in a thermodynamic MHD model using an implicit method and an explicit super time-stepping (STS) method. We found that while the STS method outperformed the implicit one with overall good results, it was not able to damp oscillatory behavior in the solution efficiently, hindering its practical use. In this follow-up work, we evaluate an easy-to-implement method for selecting a practical time step limit (PTL) for unconditionally stable schemes. This time step is used to `cycle' the operator-split thermal conduction and viscosity parabolic operators. We test the new time step with both an implicit and STS scheme for accuracy, performance, and scaling. We find that, for our test cases here, the PTL dramatically improves the STS solution, matching or improving the solution of the original implicit scheme, while retaining most of its performance and scaling advantages. The PTL shows promise to allow more accurate use of unconditionally stable schemes for parabolic operators and reliable use of STS methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.01004v1-abstract-full').style.display = 'none'; document.getElementById('2403.01004v1-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> 1 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">12 pages, 6 figures. ASTRONUM 23</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.10980">arXiv:2402.10980</a> <span> [<a href="https://arxiv.org/pdf/2402.10980">pdf</a>, <a href="https://arxiv.org/format/2402.10980">other</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="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</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"> ChemReasoner: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedback </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sprueill%2C+H+W">Henry W. Sprueill</a>, <a href="/search/cs?searchtype=author&query=Edwards%2C+C">Carl Edwards</a>, <a href="/search/cs?searchtype=author&query=Agarwal%2C+K">Khushbu Agarwal</a>, <a href="/search/cs?searchtype=author&query=Olarte%2C+M+V">Mariefel V. Olarte</a>, <a href="/search/cs?searchtype=author&query=Sanyal%2C+U">Udishnu Sanyal</a>, <a href="/search/cs?searchtype=author&query=Johnston%2C+C">Conrad Johnston</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+H">Hongbin Liu</a>, <a href="/search/cs?searchtype=author&query=Ji%2C+H">Heng Ji</a>, <a href="/search/cs?searchtype=author&query=Choudhury%2C+S">Sutanay Choudhury</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="2402.10980v4-abstract-short" style="display: inline;"> The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future. We introduce an AI-guided computational screening framework unifying linguistic reasoning with quantum-chemistry based feedback from 3D atomistic representations. Our approach formulates catalyst discovery as an uncertain environment where an agent… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.10980v4-abstract-full').style.display = 'inline'; document.getElementById('2402.10980v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.10980v4-abstract-full" style="display: none;"> The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future. We introduce an AI-guided computational screening framework unifying linguistic reasoning with quantum-chemistry based feedback from 3D atomistic representations. Our approach formulates catalyst discovery as an uncertain environment where an agent actively searches for highly effective catalysts via the iterative combination of large language model (LLM)-derived hypotheses and atomistic graph neural network (GNN)-derived feedback. Identified catalysts in intermediate search steps undergo structural evaluation based on spatial orientation, reaction pathways, and stability. Scoring functions based on adsorption energies and reaction energy barriers steer the exploration in the LLM's knowledge space toward energetically favorable, high-efficiency catalysts. We introduce planning methods that automatically guide the exploration without human input, providing competitive performance against expert-enumerated chemical descriptor-based implementations. By integrating language-guided reasoning with computational chemistry feedback, our work pioneers AI-accelerated, trustworthy catalyst discovery. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.10980v4-abstract-full').style.display = 'none'; document.getElementById('2402.10980v4-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">9 pages, accepted by ICML 2024, final version</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.04061">arXiv:2306.04061</a> <span> [<a href="https://arxiv.org/pdf/2306.04061">pdf</a>, <a href="https://arxiv.org/format/2306.04061">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</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/10.1145/3617694.3623254">10.1145/3617694.3623254 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Deploying a Robust Active Preference Elicitation Algorithm on MTurk: Experiment Design, Interface, and Evaluation for COVID-19 Patient Prioritization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Johnston%2C+C+M">Caroline M. Johnston</a>, <a href="/search/cs?searchtype=author&query=Vossler%2C+P">Patrick Vossler</a>, <a href="/search/cs?searchtype=author&query=Blessenohl%2C+S">Simon Blessenohl</a>, <a href="/search/cs?searchtype=author&query=Vayanos%2C+P">Phebe Vayanos</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="2306.04061v2-abstract-short" style="display: inline;"> Preference elicitation leverages AI or optimization to learn stakeholder preferences in settings ranging from marketing to public policy. The online robust preference elicitation procedure of arXiv:2003.01899 has been shown in simulation to outperform various other elicitation procedures in terms of effectively learning individuals' true utilities. However, as with any simulation, the method makes… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.04061v2-abstract-full').style.display = 'inline'; document.getElementById('2306.04061v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.04061v2-abstract-full" style="display: none;"> Preference elicitation leverages AI or optimization to learn stakeholder preferences in settings ranging from marketing to public policy. The online robust preference elicitation procedure of arXiv:2003.01899 has been shown in simulation to outperform various other elicitation procedures in terms of effectively learning individuals' true utilities. However, as with any simulation, the method makes a series of assumptions that cannot easily be verified to hold true beyond simulation. Thus, we propose to validate the robust method's performance using real users, focusing on the particular challenge of selecting policies for prioritizing COVID-19 patients for scarce hospital resources during the pandemic. To this end, we develop an online platform for preference elicitation where users report their preferences between alternatives over a moderate number of pairwise comparisons chosen by a particular elicitation procedure. We recruit 193 Amazon Mechanical Turk (MTurk) workers to report their preferences and demonstrate that the robust method outperforms asking random queries by 21%, the next best performing method in the simulated results of arXiv:2003.01899, in terms of recommending policies with a higher utility. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.04061v2-abstract-full').style.display = 'none'; document.getElementById('2306.04061v2-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> 6 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </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">10 pages, 5 figures, 1 table</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO 2023). Association for Computing Machinery, Article 31, (2023) 1-10 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2107.03078">arXiv:2107.03078</a> <span> [<a href="https://arxiv.org/pdf/2107.03078">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Can Connected Autonomous Vehicles really improve mixed traffic efficiency in realistic scenarios? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Garg%2C+M">Mohit Garg</a>, <a href="/search/cs?searchtype=author&query=Johnston%2C+C">Cian Johnston</a>, <a href="/search/cs?searchtype=author&query=Bouroche%2C+M">M茅lanie Bouroche</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="2107.03078v2-abstract-short" style="display: inline;"> Connected autonomous vehicles (CAVs) can supplement the information from their own sensors with information from surrounding CAVs for decision making and control. This has the potential to improve traffic efficiency. CAVs face additional challenges in their driving, however, when they interact with human-driven vehicles (HDVs) in mixed-traffic environments due to the uncertainty in human's driving… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.03078v2-abstract-full').style.display = 'inline'; document.getElementById('2107.03078v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.03078v2-abstract-full" style="display: none;"> Connected autonomous vehicles (CAVs) can supplement the information from their own sensors with information from surrounding CAVs for decision making and control. This has the potential to improve traffic efficiency. CAVs face additional challenges in their driving, however, when they interact with human-driven vehicles (HDVs) in mixed-traffic environments due to the uncertainty in human's driving behavior e.g. larger reaction times, perception errors, etc. While a lot of research has investigated the impact of CAVs on traffic safety and efficiency at different penetration rates, all have assumed either perfect communication or very simple scenarios with imperfect communication. In practice, the presence of communication delays and packet losses means that CAVs might receive only partial information from surrounding vehicles, and this can have detrimental effects on their performance. This paper investigates the impact of CAVs on traffic efficiency in realistic communication and road network scenarios (i.e. imperfect communication and large-scale road network). We analyze the effect of unreliable communication links on CAVs operation in mixed traffic with various penetration rates and evaluate traffic performance in congested traffic scenarios on a large-scale road network (the M50 motorway, in Ireland). Results show that CAVs can significantly improve traffic efficiency in congested traffic scenarios at high penetration rates. The scale of the improvement depends on communication reliability, with a packet drop rate of 70% leading to an increase in traffic congestion by 28.7% and 11.88% at 40% and 70% penetration rates respectively compared to perfect communication. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.03078v2-abstract-full').style.display = 'none'; document.getElementById('2107.03078v2-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 7 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2003.12476">arXiv:2003.12476</a> <span> [<a href="https://arxiv.org/pdf/2003.12476">pdf</a>, <a href="https://arxiv.org/format/2003.12476">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Materials Science">cond-mat.mtrl-sci</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/s41597-020-00638-4">10.1038/s41597-020-00638-4 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Huber%2C+S+P">Sebastiaan. P. Huber</a>, <a href="/search/cs?searchtype=author&query=Zoupanos%2C+S">Spyros Zoupanos</a>, <a href="/search/cs?searchtype=author&query=Uhrin%2C+M">Martin Uhrin</a>, <a href="/search/cs?searchtype=author&query=Talirz%2C+L">Leopold Talirz</a>, <a href="/search/cs?searchtype=author&query=Kahle%2C+L">Leonid Kahle</a>, <a href="/search/cs?searchtype=author&query=H%C3%A4uselmann%2C+R">Rico H盲uselmann</a>, <a href="/search/cs?searchtype=author&query=Gresch%2C+D">Dominik Gresch</a>, <a href="/search/cs?searchtype=author&query=M%C3%BCller%2C+T">Tiziano M眉ller</a>, <a href="/search/cs?searchtype=author&query=Yakutovich%2C+A+V">Aliaksandr V. Yakutovich</a>, <a href="/search/cs?searchtype=author&query=Andersen%2C+C+W">Casper W. Andersen</a>, <a href="/search/cs?searchtype=author&query=Ramirez%2C+F+F">Francisco F. Ramirez</a>, <a href="/search/cs?searchtype=author&query=Adorf%2C+C+S">Carl S. Adorf</a>, <a href="/search/cs?searchtype=author&query=Gargiulo%2C+F">Fernando Gargiulo</a>, <a href="/search/cs?searchtype=author&query=Kumbhar%2C+S">Snehal Kumbhar</a>, <a href="/search/cs?searchtype=author&query=Passaro%2C+E">Elsa Passaro</a>, <a href="/search/cs?searchtype=author&query=Johnston%2C+C">Conrad Johnston</a>, <a href="/search/cs?searchtype=author&query=Merkys%2C+A">Andrius Merkys</a>, <a href="/search/cs?searchtype=author&query=Cepellotti%2C+A">Andrea Cepellotti</a>, <a href="/search/cs?searchtype=author&query=Mounet%2C+N">Nicolas Mounet</a>, <a href="/search/cs?searchtype=author&query=Marzari%2C+N">Nicola Marzari</a>, <a href="/search/cs?searchtype=author&query=Kozinsky%2C+B">Boris Kozinsky</a>, <a href="/search/cs?searchtype=author&query=Pizzi%2C+G">Giovanni Pizzi</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="2003.12476v1-abstract-short" style="display: inline;"> The ever-growing availability of computing power and the sustained development of advanced computational methods have contributed much to recent scientific progress. These developments present new challenges driven by the sheer amount of calculations and data to manage. Next-generation exascale supercomputers will harden these challenges, such that automated and scalable solutions become crucial.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.12476v1-abstract-full').style.display = 'inline'; document.getElementById('2003.12476v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2003.12476v1-abstract-full" style="display: none;"> The ever-growing availability of computing power and the sustained development of advanced computational methods have contributed much to recent scientific progress. These developments present new challenges driven by the sheer amount of calculations and data to manage. Next-generation exascale supercomputers will harden these challenges, such that automated and scalable solutions become crucial. In recent years, we have been developing AiiDA (http://www.aiida.net), a robust open-source high-throughput infrastructure addressing the challenges arising from the needs of automated workflow management and data provenance recording. Here, we introduce developments and capabilities required to reach sustained performance, with AiiDA supporting throughputs of tens of thousands processes/hour, while automatically preserving and storing the full data provenance in a relational database making it queryable and traversable, thus enabling high-performance data analytics. AiiDA's workflow language provides advanced automation, error handling features and a flexible plugin model to allow interfacing with any simulation software. The associated plugin registry enables seamless sharing of extensions, empowering a vibrant user community dedicated to making simulations more robust, user-friendly and reproducible. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.12476v1-abstract-full').style.display = 'none'; document.getElementById('2003.12476v1-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> 24 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Scientific Data 7, 300 (2020) </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> 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