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RESEARCH PAPER · npj Computational Materials · 2022

Training data selection for accuracy and transferability of interatomic potentials

David Montes de Oca Zapiain, Mitchell A. Wood, Nicholas Lubbers, Carlos Z. Pereyra, Aidan P. Thompson, Danny Pérez

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Training data selection for accuracy and transferability of interatomic potentialsOriginalBeyond Test-Set Error: A Conceptual Framework for Evaluating ML Potential Transferability Under Distribution Shift0Molecular dynamics of high pressure tin phases: Empirical and machine learned interatomic potentials1Active sampling for neural network potentials: Accelerated simulations of shear-induced deformation in Cu–Ni multilayers5Systematic atomic structure datasets for machine learning potentials: Application to defects in magnesium45Machine learned interatomic potential for dispersion strengthened plasma facing components23Fast proper orthogonal descriptors for many-body interatomic potentials8AL4GAP: Active learning workflow for generating DFT-SCAN accurate machine-learning potentials for combinatorial molten salt mixtures16A deep learning interatomic potential suitable for simulating radiation damage in bulk tungsten21Exploring model complexity in machine learned potentials for simulated properties12Improving stability and transferability of machine learned interatomic potentials using physically informed bounding potentials4Machine learning is funny but physics makes the money: How machine-learning potentials can advance computer-aided materials design in metallurgy8Accelerating FEM-Based Corrosion Predictions Using Machine Learning15Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment22Assessing the accuracy of machine learning interatomic potentials in predicting the elemental orderings: A case study of Li-Al alloys20U-PASS: An uncertainty-guided deep learning pipeline for automated sleep staging15Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling114Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices30Collaboration on Machine-Learned Potentials with IPSuite: A Modular Framework for Learning-on-the-Fly13Active learning for SNAP interatomic potentials via Bayesian predictive uncertainty4Enhancing Molecular Energy Predictions with Physically Constrained Modifications to the Neural Network Potential8Machine learning-assisted substrate binding pocket engineering based on structural information7Improving Machine Learned Force Fields for Complex Fluids through Enhanced Sampling: A Liquid Crystal Case Study6Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning16Electronic structure simulations in the cloud computing environment10Data Generation for Machine Learning Interatomic Potentials and Beyond176Machine learned interatomic potentials for gas-metal interactions5Parameter uncertainties for imperfect surrogate models in the low-noise regime7Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials1Uncertainty in the era of machine learning for atomistic modeling11Unified approach to generating a training set for machine learning interatomic potentials: The case of BCC tungsten5Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications19Modeling the impact of structure and coverage on the reactivity of realistic heterogeneous catalysts39Impact of crystal structure symmetry in training datasets on GNN-based energy assessments for chemically disordered CsPbI33When more data hurts: Optimizing data coverage while mitigating diversity-induced underfitting in an ultrafast machine-learned potential3Shadow Molecular Dynamics for a Charge-Potential Equilibration Model5Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory32Deep Learning-Enhanced Titanium Potential Models for Accurate Molecular Dynamics3Application of machine learning interatomic potentials in heterogeneous catalysis25Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials10Uncertainty quantification for misspecified machine learned interatomic potentials18Opportunities for Connecting Computational and Experimental Approaches to Design Zeolite Catalysts for Ethanol-to-Olefin Reactions4Moment tensor potential and equivariant tensor network potential with explicit dispersion interactions3Modeling Diffusion in Metal–Organic Frameworks Using On-the-fly Probability Enhanced Sampling-Based Machine Learning Potentials1Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost8Global properties of the energy landscape: a testing and training arena for machine learned potentials5Breaking the Barriers of Molecular Dynamics With Deep‐Learning: Opportunities, Pitfalls, and How to Navigate Them7Using structure to evaluate the accuracy and transferability of machine learning potentials of biomolecules0Unsupervised atomic data mining via multi-kernel graph autoencoders for machine learning force fields0Environment-adaptive machine-learned force fields for materials under extreme conditions: hafnium and hafnium dioxide polymorphs0A physics-driven statistical mechanics framework for many-body thermodynamic and transport property computation2A dislocation-aware active learned interatomic potential for Bi1−xSbx0Transferable Neural Network Potential for Elastic and Vibrational Properties of Carbon, Hydrogen, Oxygen, and Nitrogen-Based Two Dimensional Covalent Organic Frameworks0Stoichiometry dependent properties of cerium hydride: An active learning developed interatomic potential study1Role of spin-crossover phenomena in DFT training data for robust machine-learning interatomic potentials0Full-stack quantification of variability in predicting ion transport properties using machine-learned interatomic potentials0Transferability of foundation MACE models to amorphous chemistry: Where they work and where they fail0

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  1. Beyond Test-Set Error: A Conceptual Framework for Evaluating ML Potential Transferability Under Distribution Shift

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  2. Molecular dynamics of high pressure tin phases: Empirical and machine learned interatomic potentials

    AIP conference proceedings · 2023-01-01

    Mary Alice Cusentino, Benjamin T. Nebgen, Kipton M. Barros, Justin S. Smith, John D. Shimanek, Alice E. A. Allen, Aidan P. Thompson, Saryu Jindal Fensin, J. Matthew D. Lane

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  3. Active sampling for neural network potentials: Accelerated simulations of shear-induced deformation in Cu–Ni multilayers

    The Journal of Chemical Physics · 2023-02-22

    Henry W. Sprueill, Jenna A. Bilbrey, Qin Pang, Peter V. Sushko

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  4. Systematic atomic structure datasets for machine learning potentials: Application to defects in magnesium

    Physical review. B./Physical review. B · 2023-03-13

    Marvin Poul, Liam Huber, Erik Bitzek, Jörg Neugebauer

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  5. Machine learned interatomic potential for dispersion strengthened plasma facing components

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    Ember L. Sikorski, Mary Alice Cusentino, Megan McCarthy, Julien Tranchida, Mitchell A. Wood, Aidan P. Thompson

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  6. Fast proper orthogonal descriptors for many-body interatomic potentials

    Physical review. B./Physical review. B · 2023-04-12

    Ngoc Cuong Nguyen

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  7. AL4GAP: Active learning workflow for generating DFT-SCAN accurate machine-learning potentials for combinatorial molten salt mixtures

    The Journal of Chemical Physics · 2023-07-10

    Jicheng Guo, Vanessa Woo, David A. Andersson, Nathaniel C. Hoyt, Mark A. Williamson, Ian T Foster, Chris J. Benmore, Nicholas E. Jackson, Ganesh Sivaraman

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  8. A deep learning interatomic potential suitable for simulating radiation damage in bulk tungsten

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    Changjie Ding, Yawei Lei, Xiaoyang Wang, Xiaolin Li, Xiang-Yan Li, Xiang-Yan Li, Xiang-Yan Li, Yange Zhang, Xuebang Wu, C.S. Liu, Xuebang Wu

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  9. Exploring model complexity in machine learned potentials for simulated properties

    Journal of materials research/Pratt's guide to venture capital sources · 2023-09-18

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  10. Improving stability and transferability of machine learned interatomic potentials using physically informed bounding potentials

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  11. Machine learning is funny but physics makes the money: How machine-learning potentials can advance computer-aided materials design in metallurgy

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  12. Accelerating FEM-Based Corrosion Predictions Using Machine Learning

    Journal of The Electrochemical Society · 2024-01-01

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  13. Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment

    Journal of Chemical Theory and Computation · 2024-02-02

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  14. Assessing the accuracy of machine learning interatomic potentials in predicting the elemental orderings: A case study of Li-Al alloys

    Acta Materialia · 2024-02-07

    Yunsheng Liu, Yifei Mo

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  15. U-PASS: An uncertainty-guided deep learning pipeline for automated sleep staging

    Computers in Biology and Medicine · 2024-02-23

    Elisabeth R. M. Heremans, Nabeel Seedat, Bertien Buyse, Dries Testelmans, Mihaela van der Schaar, Maarten De Vos

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  16. Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling

    npj Computational Materials · 2024-02-26

    Ji Qi, Tsz Wai Ko, Brandon C. Wood, Tuan Anh Pham, Shyue Ping Ong

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  17. Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices

    The Journal of Physical Chemistry C · 2024-03-20

    Tristan Maxson, Ademola Soyemi, Benjamin W. J. Chen, Tibor Szilvási

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  18. Collaboration on Machine-Learned Potentials with IPSuite: A Modular Framework for Learning-on-the-Fly

    The Journal of Physical Chemistry B · 2024-04-03

    Fabian Zills, Moritz R. Schäfer, Nico Segreto, Johannes Kästner, Christian Holm, Samuel Tovey

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  19. Active learning for SNAP interatomic potentials via Bayesian predictive uncertainty

    Computational Materials Science · 2024-05-14

    Logan Williams, Khachik Sargsyan, Andrew Rohskopf, Habib N. Najm

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  20. Enhancing Molecular Energy Predictions with Physically Constrained Modifications to the Neural Network Potential

    Journal of Chemical Theory and Computation · 2024-06-03

    Weiqiang Fu, Yujie Mo, Xiao Yi, Chang Liu, Feng Zhou, Yan Wang, Jielong Zhou, Yingsheng Zhang

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  21. Machine learning-assisted substrate binding pocket engineering based on structural information

    Briefings in Bioinformatics · 2024-07-24

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  22. Improving Machine Learned Force Fields for Complex Fluids through Enhanced Sampling: A Liquid Crystal Case Study

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  23. Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning

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  25. Data Generation for Machine Learning Interatomic Potentials and Beyond

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  26. Machine learned interatomic potentials for gas-metal interactions

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