Gaussian Approximation Potentials: a brief tutorial introduction

2015/02/04 by Bartók, Albert P., Csányi, Gábor · 8 citations
#Chemical Physics (physics.chem-ph) #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci)

paper · doi:10.48550/arxiv.1502.01366

Abstract

We present a swift walk-through of our recent work that uses machine learning to fit interatomic potentials based on quantum mechanical data. We describe our Gaussian Approximation Potentials (GAP) framework, discussing a variety of descriptors, how to train the model on total energies and derivatives and the simultaneous use of multiple models. We also show a small example using QUIP, the software sandbox implementation of GAP that is available for non-commercial use.

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