3 papers
cond-mat.mtrl-sci2023
Gaussian Approximation Potentials: theory, software implementation and application examples
Sascha Klawohn, Gábor Csányi, James P. Darby +3
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 im…
cond-mat.mtrl-sci2022
Massively Parallel Fitting of Gaussian Approximation Potentials
Sascha Klawohn, James R. Kermode, Albert P. Bartók
We present a data-parallel software package for fitting Gaussian Approximation Potentials (GAPs) on multiple nodes using the ScaLAPACK library with MPI and OpenMP. Until now the ma…
cond-mat.mtrl-sci2021
Synergistic coupling in ab initio-machine learning simulations of dislocations
Petr Grigorev, Alexandra M. Goryaeva, Mihai-Cosmin Marinica +2
Ab initio simulations of dislocations are essential to build quantitative models of material strength, but the required system sizes are often at or beyond the limit of existing me…