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Keyword: structural relaxation ×Clear all

1 claim from 1 paper

1. Materials Science › Machine Learning in Materials Science

   Active learning of linearly parametrized interatomic potentials

   Podryabinkin and Shapeev · Computational Materials Science · 2017

   The paper introduces an active learning method, based on the D-optimality criterion, for fitting machine learning interatomic potentials, and applies it to molecular dynamics and structure relaxation.

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   1. UncheckedThe authors' active learning method trains interatomic potentials during a simulation, avoiding extrapolation and keeping accuracy without any significant decrease.“It is shown that the proposed active learning approach is highly efficient in training potentials on the fly, ensuring that no extrapolation is attempted and leading to a completely reliable atomistic simulation without any significant decrease in accuracy.”