Massively parallel fitting of Gaussian approximation potentials

Autor: Sascha Klawohn, James R Kermode, Albert P Bartók

Publikováno v: Machine Learning: Science and Technology, Vol 4, Iss 1, p 015020 (2023)

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 maximum training set size for GAP models has been limited by the availab

Externí odkaz: https://doaj.org/article/9646075d7fe245e88d9be4e46e2012a2

Comment on 'Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions' [J. Chem. Phys. 156, 034302 (2022)]

Autor: Sergey N. Pozdnyakov, Michael J. Willatt, Albert P. Bartók, Christoph Ortner, Gábor Csányi, Michele Ceriotti

The “quasi-constant” smooth overlap of atomic position and atom-centered symmetry function fingerprint manifolds recently discovered by Parsaeifard and Goedecker [J. Chem. Phys. 156, 034302 (2022)] are closely related to the degenerate pairs of c

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bcfd4683bd671e32fd14b528c1989040

Gaussian Process Regression for Materials and Molecules

Autor: David M. Wilkins, Noam Bernstein, Gábor Csányi, Albert P. Bartók, Volker L. Deringer, Michele Ceriotti

Publikováno v: Deringer, V L, Bartók, A P, Bernstein, N, Wilkins, D M, Ceriotti, M & Csányi, G 2021, ' Gaussian Process Regression for Materials and Molecules ', Chemical Reviews, vol. 121, no. 16, pp. 10073-10141 . https://doi.org/10.1021/acs.chemrev.1c00022
Chemical Reviews

We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the constructio

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df4a7aa18c2e0b310ffeebbf6bef2f12
https://doi.org/10.1021/acs.chemrev.1c00022

Machine Learning a General-Purpose Interatomic Potential for Silicon

Autor: Albert P. Bartók, James Kermode, Noam Bernstein, Gábor Csányi

Publikováno v: Physical Review X, Vol 8, Iss 4, p 041048 (2018)

The success of first-principles electronic-structure calculation for predictive modeling in chemistry, solid-state physics, and materials science is constrained by the limitations on simulated length scales and timescales due to the computational cos

Externí odkaz: https://doaj.org/article/ed4c68df5ee84d71a0992bf2aaea2dbe

Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C$_{60}$

Autor: Gábor Csányi, Patricia Hernández-León, Tapio Ala-Nissila, Miguel A. Caro, Albert P. Bartók, Xi Chen, Heikki Muhli

We present a comprehensive methodology to enable the addition of van der Waals (vdW) corrections to machine learning (ML) atomistic force fields. Using a Gaussian approximation potential (GAP) [Bartok et al., Phys. Rev. Lett. 104, 136403 (2010)10.110

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::43a385b317acd17ee3cc53f3665cd64c