CUED Publications database

Gaussian approximation potentials: A brief tutorial introduction

Bartók, AP and Csányi, G (2015) Gaussian approximation potentials: A brief tutorial introduction. International Journal of Quantum Chemistry. ISSN 0020-7608

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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, discuss a variety of descriptors, how to train the model on total energies and derivatives, and the simultaneous use of multiple models of different complexity. We also show a small example using QUIP, the software sandbox implementation of GAP that is available for noncommercial use.

Item Type: Article
Uncontrolled Keywords: ab initio Atomic environments Gaussian process Interatomic potentials Machine learning
Subjects: UNSPECIFIED
Divisions: Div C > Applied Mechanics
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 18:59
Last Modified: 16 Nov 2017 02:19
DOI: