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Over the last few years, key architectural advances have been proposed for neural network interatomic potentials (NNIPs), such as incorporating message-passing networks, equivariance, or many-body expansion terms.
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Christian Devereux, Justin S. Smith, Kate K. Huddleston, Kipton Barros, Roman Zubatyuk, Olexandr Isayev, and Adrian E. Roitberg · 2020
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ForceNet: A graph neural network for large-scale quantum calculations
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Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gomez-Bombarelli, and Tommi Jaakkola · 2022
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How to validate machine-learned interatomic potentials
Joe D Morrow, John LA Gardner, and Volker L Deringer · 2022
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Neural potentials of proteins extrapolate beyond training data
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Bayesian, frequentist, and information geometric approaches to parametric uncertainty quantification of classical empirical interatomic potentials
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