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arXiv · 2112.10434

Exploring the robust extrapolation of high-dimensional machine learning potentials

Claudio Zeni·Andrea Anelli·Aldo Glielmo·Kevin Rossi
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Abstract

We show that, contrary to popular assumptions, predictions from machine learning potentials built upon high-dimensional atom-density representations almost exclusively occur in regions of the representation space which lie outside the convex hull defined by the training set points. We then propose a perspective to rationalize the domain of robust extrapolation and accurate prediction of atomistic machine learning potentials in terms of the probability density induced by training points in the representation space

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Claudio Zeni, Andrea Anelli, Aldo Glielmo, Kevin Rossi. 2021-12-20. Exploring the robust extrapolation of high-dimensional machine learning potentials. https://doi.org/10.1103/physrevb.105.165141

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