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The rapid adoption of machine learning (ML) in domain sciences necessitates best practices and standardized benchmarking for performance evaluation.
“The Inorganic Crystal Structure Data Base”
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Erik Bitzek, Pekka Koskinen, Franz Gähler, Michael Moseler and Peter Gumbsch · 2006
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“Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces”
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“AFLOW: An Automatic Framework for High-Throughput Materials Discovery”
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“Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning”
Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller and O. von Lilienfeld · 2012
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“Accuracy of Density Functional Theory in Predicting Formation Energies of Ternary Oxides from Binary Oxides and Its Implication on Phase Stability”
Geoffroy Hautier, Shyue Ong, Anubhav Jain, Charles. Moore and Gerbrand Ceder · 2012
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“Commentary: The Materials Project: A Materials Genome Approach to Accelerating Materials Innovation”
Anubhav Jain et al · 2013
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“Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)”
James. Saal, Scott Kirklin, Muratahan Aykol, Bryce Meredig and C. Wolverton · 2013
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“How to Represent Crystal Structures for Machine Learning: Towards Fast Prediction of Electronic Properties”
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“Computational Screening of All Stoichiometric Inorganic Materials”
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“The thermodynamic scale of inorganic crystalline metastability”
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“The Optimal One Dimensional Periodic Table: A Modified Pettifor Chemical Scale from Data Mining”
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“A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials”
Logan Ward, Ankit Agrawal, Alok Choudhary and Christopher Wolverton · 2016
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Stefan Depeweg, José Hernández-Lobato, Finale Doshi-Velez and Steffen Udluft · 2017
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“Neural message passing for quantum chemistry”
Justin Gilmer, Samuel Schoenholz, Patrick Riley, Oriol Vinyals and George Dahl · 2017
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“Attention Is All You Need”, 2017
Ashish Vaswani et al · 2017
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“Including Crystal Structure Attributes in Machine Learning Models of Formation Energies via Voronoi Tessellations”
Logan Ward et al · 2017
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“NOMAD: The FAIR Concept for Big Data-Driven Materials Science”
Claudia Draxl and Matthias Scheffler · 2018
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“NERSC 2018 Workload Analytis” Accessed: 2022-05-15, https://portal.nersc.gov/project/m888/nersc10/workload/N10_Workload_Analysis.latest.pdf , 2018
Brian Austin and al · 2018
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“Machine Learning a General-Purpose Interatomic Potential for Silicon”
Albert. Bartók, James Kermode, Noam Bernstein and Gábor Csányi · 2018
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“MoleculeNet: A Benchmark for Molecular Machine Learning”
Zhenqin Wu et al · 2018
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“Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery”
Bryce Meredig et al · 2018
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“Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds”, 2018
Nathaniel Thomas et al · 2018
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“Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties”
Tian Xie and Jeffrey. Grossman · 2018
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“Thermodynamic Limit for Synthesis of Metastable Inorganic Materials”
Muratahan Aykol, Shyam. Dwaraknath, Wenhao Sun and Kristin. Persson · 2018
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“Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data”
Ekin Cubuk, Austin Sendek and Evan Reed · 2019
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“Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals”
Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng and Shyue Ong · 2019
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“Atomic Cluster Expansion for Accurate and Transferable Interatomic Potentials”
Ralf Drautz · 2019
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“A Critical Examination of Compound Stability Predictions from Machine-Learned Formation Energies”
Christopher. Bartel et al · 2020
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“A General-Purpose Machine-Learning Force Field for Bulk and Nanostructured Phosphorus”
Volker. Deringer, Miguel. Caro and Gábor Csányi · 2020
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“Cautionary guidelines for machine learning studies with combinatorial datasets”
Andrew Zahrt, Jeremy Henle and Scott Denmark · 2020
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“Predicting Materials Properties without Crystal Structure: Deep Representation Learning from Stoichiometry”
Rhys.. Goodall and Alpha. Lee · 2020
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“Matsciml: A broad, multi-task benchmark for solid-state materials modeling”
Kin Lee et al · 2023
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“AdsorbML: Accelerating Adsorption Energy Calculations with Machine Learning”, 2023
Janice Lan et al · 2023
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“A Critical Examination of Robustness and Generalizability of Machine Learning Prediction of Materials Properties”
Kangming Li, Brian DeCost, Kamal Choudhary, Michael Greenwood and Jason Hattrick-Simpers · 2023
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“Exploiting Redundancy in Large Materials Datasets for Efficient Machine Learning with Less Data”
Kangming Li et al · 2023
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Alexander Dunn, Qi Wang, Alex Ganose, Daniel Dopp and Anubhav Jain · 2020
Cited alongside, same era.
“Retrospective on a Decade of Machine Learning for Chemical Discovery”
O. von Lilienfeld and Kieron Burke · 2020
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“Accelerating Materials Discovery with Bayesian Optimization and Graph Deep Learning”
Yunxing Zuo et al · 2021
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“On the Robustness of Generalization of Drug–Drug Interaction Models”
Rogia Kpanou, Mazid Osseni, Prudencio Tossou, Francois Laviolette and Jacques Corbeil · 2021
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“Open Catalyst 2020 (OC20) Dataset and Community Challenges”
Lowik Chanussot et al · 2021
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“Predicting Stable Crystalline Compounds Using Chemical Similarity”
Hai-Chen Wang, Silvana Botti and Miguel.. Marques · 2021
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“Atomistic Line Graph Neural Network for Improved Materials Property Predictions”
Kamal Choudhary and Brian DeCost · 2021
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“A foundation model for atomistic materials chemistry”
Ilyes Batatia et al · 2023
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“Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations” Survey Certification
Xiang Fu et al · 2023
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“Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs”
Yi-Lun Liao and Tess Smidt · 2023
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Saro Passaro and C Zitnick · 2023
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Amil Merchant et al · 2023
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“Unified Graph Neural Network Force-Field for the Periodic Table: Solid State Applications”
Kamal Choudhary et al · 2023
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“Chemical Reaction Networks and Opportunities for Machine Learning”
Mingjian Wen et al · 2023
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“Improving machine-learning models in materials science through large datasets”
Jonathan Schmidt et al · 2024
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Janosh Riebesell, T. Surta, Rhys Goodall, Michael Gaultois and Alpha. Lee · 2024
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“Goldilocks convergence tools and best practices for numerical approximations in Density Functional Theory calculations — gow.epsrc.ukri.org” [Accessed 03-11-2024], https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/Z530657/1
2024
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Kamal Choudhary et al · 2024
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Anuroop Sriram et al · 2024
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Abhijith Parackal, Rhys Goodall, Felix Faber and Rickard Armiento · 2024
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“Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery”
Anthony Cheetham and Ram Seshadri · 2024
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Bowen Deng et al · 2024
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“Thermal Conductivity Predictions with Foundation Atomistic Models”
Balázs Póta, Paramvir Ahlawat, Gábor Csányi and Michele Simoncelli · 2024
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“EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations”
Yi-Lun Liao, Brandon Wood, Abhishek Das* and Tess Smidt* · 2024
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“Generalizing denoising to non-equilibrium structures improves equivariant force fields”
Yi-Lun Liao, Tess Smidt, Muhammed Shuaibi and Abhishek Das · 2024
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“Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models”
Luis Barroso-Luque et al · 2024
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“Orb: A Fast, Scalable Neural Network Potential”
Mark Neumann et al · 2024
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“Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations”
Yutack Park, Jaesun Kim, Seungwoo Hwang and Seungwu Han · 2024
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“Mattersim: A deep learning atomistic model across elements, temperatures and pressures”
Han Yang et al · 2024
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Eric.-Y. Yuan et al · 2024
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