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arXiv · 2207.04009

Systematic Atomic Structure Datasets for Machine Learning Potentials: Application to Defects in Magnesium

Marvin Poul·Liam Huber·Erik Bitzek·Jörg Neugebauer
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Abstract

We present a physically motivated strategy for the construction of training sets for transferable machine learning interatomic potentials. It is based on a systematic exploration of all possible space groups in random crystal structures, together with deformations of cell shape, size, and atomic positions. The resulting potentials turn out to be unbiased and generically applicable to studies of bulk defects without including any defect structures in the training set or employing any additional Active Learning. Using this approach we construct transferable potentials for pure Magnesium that reproduce the properties of hexagonal closed packed (hcp) and body centered cubic (bcc) polymorphs very well. In the process we investigate how different types of training structures impact the properties and the predictive power of the resulting potential.

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Marvin Poul, Liam Huber, Erik Bitzek, Jörg Neugebauer. 2022-07-08. Systematic Atomic Structure Datasets for Machine Learning Potentials: Application to Defects in Magnesium. https://doi.org/10.1103/physrevb.107.104103

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