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Even though Bayesian neural networks offer a promising framework for modeling uncertainty, active learning and incorporating prior physical knowledge, few applications of them can be found in the context of interatomic force modeling.
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Stefan Grimme, Andreas Hansen, Jan Gerit Brandenburg, and Christoph Bannwarth · 2016
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Evgeny V. Podryabinkin and Alexander V. Shapeev · 2017
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Evgeny V. Podryabinkin, Evgeny V. Tikhonov, Alexander V. Shapeev, and Artem R. Oganov · 2019
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Konstantin Gubaev, Evgeny V. Podryabinkin, Gus L.W. Hart, and Alexander V. Shapeev · 2019
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J. Yao, W. Pan, S. Ghosh, and F. Doshi-Velez · 2019
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Igor Zozoulenko, Amritpal Singh, Sandeep Kumar Singh, Viktor Gueskine, Xavier Crispin, and Magnus Berggren · 2019
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M. Gastegger and P. Marquetand · 2020
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On-the-fly active learning of interatomic potentials for large-scale atomistic simulations
Ryosuke Jinnouchi, Kazutoshi Miwa, Ferenc Karsai, Georg Kresse, and Ryoji Asahi · 2020
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Active learning and uncertainty estimation
A. Shapeev, K. Gubaev, E. Tsymbalov, and E. Podryabinkin · 2020
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Emir Kocer, Tsz Wai Ko, and Jörg Behler · 2022
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E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
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