Journal
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
Volume 16, Issue 8, Pages 4757-4775Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jctc.0c00355
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Funding
- EPSRC [EP/P006175/1] Funding Source: UKRI
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Machine learning encompasses tools and algorithms that 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.
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