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Machine learning encompasses a set of tools and algorithms which are nowbecoming popular in almost all scientific and technological fields. This istrue for molecular dynamics as well, where machine learning offers promises ofextracting valuable information from the enormous amounts of data generated bysimulation of complex systems. We provide here a review of our currentunderstanding of goals, benefits, and limitations of machine learningtechniques for computational studies on atomistic systems, focusing on theconstruction of empirical force fields from ab-initio databases and thedetermination of reaction coordinates for free energy computation and enhancedsampling.

Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems