1 citations · 1 across the 3 of their papers we have counts for
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★ 1 cited
Optimal data generation for machine learned interatomic potentials
Connor Allen, Albert P. Bartók
Machine learning interatomic potentials (MLIPs) are routinely used atomic simulations, but generating databases of atomic configurations used in fitting these models is a laborious…
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…