Gaussian Approximation Potentials: theory, software implementation and application examples
Materials Science
2023-10-09 v1 Computational Physics
Abstract
Gaussian Approximation Potentials 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 MPI parallelisation of the fitting code enabling its use on thousands of CPU cores and compression of descriptors to eliminate the poor scaling with the number of different chemical elements.
Cite
@article{arxiv.2310.03921,
title = {Gaussian Approximation Potentials: theory, software implementation and application examples},
author = {Sascha Klawohn and Gábor Csányi and James P. Darby and James R. Kermode and Miguel A. Caro and Albert P. Bartók},
journal= {arXiv preprint arXiv:2310.03921},
year = {2023}
}