Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems
arXiv:2004.06950
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.
References in corpus (7)
- PLUMED: a portable plugin for free-energy calculations with molecular dynamics
- Machine Learning Unifies the Modelling of Materials and Molecules
- On-the-fly machine learning force field generation: Application to melting points
- Machine-learning based interatomic potential for amorphous carbon
- A Variational Approach to Enhanced Sampling and Free Energy Calculations
- Nonlinear Discovery of Slow Molecular Modes using State-Free Reversible VAMPnets
- Properties of low-dimensional collective variables in the molecular dynamics of biopolymers