Issue 8, 2019

How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry

Abstract

Molecular dynamics simulations are often key to the understanding of the mechanism, rate and yield of chemical reactions. One current challenge is the in-depth analysis of the large amount of data produced by the simulations, in order to produce valuable insight and general trends. In the present study, we propose to employ recent machine learning analysis tools to extract relevant information from simulation data without a priori knowledge on chemical reactions. This is demonstrated by training machine learning models to predict directly a specific outcome quantity of ab initio molecular dynamics simulations – the timescale of the decomposition of 1,2-dioxetane. The machine learning models accurately reproduce the dissociation time of the compound. Keeping the aim of gaining physical insight, it is demonstrated that, in order to make accurate predictions, the models evidence empirical rules that are, today, part of the common chemical knowledge. This opens the way for conceptual breakthroughs in chemistry where machine analysis would provide a source of inspiration to humans.

Graphical abstract: How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry

Supplementary files

Article information

Article type
Edge Article
Submitted
10 Okt 2018
Accepted
21 Dez 2018
First published
21 Dez 2018
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2019,10, 2298-2307

How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry

F. Häse, I. Fdez. Galván, A. Aspuru-Guzik, R. Lindh and M. Vacher, Chem. Sci., 2019, 10, 2298 DOI: 10.1039/C8SC04516J

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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