Gaussian approximation potentials: A brief tutorial introduction

被引：492

作者：

Bartok, Albert P. ^{[1
]}

Csanyi, Gabor ^{[1
]}

机构：

[1] Univ Cambridge, Engn Lab, Cambridge CB2 1PZ, England

来源：

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY | 2015年 / 115卷 / 16期

基金：

英国工程与自然科学研究理事会;

关键词：

interatomic potentials; machine learning; Gaussian process; ab initio; atomic environments; ENERGY SURFACES;

D O I：

10.1002/qua.24927

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

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. (c) 2015 Wiley Periodicals, Inc.

引用

页码：1051 / 1057

页数：7

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