Gaussian approximation potentials: Theory, software implementation and application examples

Sascha Klawohn

doi:10.1063/5.0160898

Gaussian approximation potentials: Theory, software implementation and application examples

2023-11-06

24

OA

AI

DOI：10.1063/5.0160898

原文链接

原文求助

摘要

En 中文

Gaussian Approximation Potentials (GAPs) are a class of Machine Learned Interatomic Potentials routinely used to model materials and molecular systems on the atomic scale. The software implementation provides the means for both fitting models using ab initio data and using the resulting potentials in atomic simulations. Details of the GAP theory, algorithms and software are presented, together with detailed usage examples to help new and existing users. We review some recent developments to the GAP framework, including Message Passing Interface parallelisation of the fitting code enabling its use on thousands of central processing unit cores and compression of descriptors to eliminate the poor scaling with the number of different chemical elements. (c) 2023 Author(s)

Keyword:

EFFICIENT

ACCURACY

SOLIDS

Gaussian approximation potentials: Theory, software implementation and application examples

摘要

期刊

机构