Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems
Computational Physics
2020-04-16 v1 Biomolecules
Abstract
Machine learning encompasses a set of tools and algorithms which are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting valuable information from the enormous amounts of data generated by simulation of complex systems. We provide here a review of our current understanding of goals, benefits, and limitations of machine learning techniques for computational studies on atomistic systems, focusing on the construction of empirical force fields from ab-initio databases and the determination of reaction coordinates for free energy computation and enhanced sampling.
Keywords
Cite
@article{arxiv.2004.06950,
title = {Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems},
author = {Paraskevi Gkeka and Gabriel Stoltz and Amir Barati Farimani and Zineb Belkacemi and Michele Ceriotti and John Chodera and Aaron R. Dinner and Andrew Ferguson and Jean-Bernard Maillet and Hervé Minoux and Christine Peter and Fabio Pietrucci and Ana Silveira and Alexandre Tkatchenko and Zofia Trstanova and Rafal Wiewiora and Tony Leliévre},
journal= {arXiv preprint arXiv:2004.06950},
year = {2020}
}