Jump to main content
Jump to site search
PLANNED MAINTENANCE Close the message box

Scheduled maintenance work on Wednesday 27th March 2019 from 11:00 AM to 1:00 PM (GMT).

During this time our website performance may be temporarily affected. We apologise for any inconvenience this might cause and thank you for your patience.


Issue 8, 2019
Previous Article Next Article

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

Author affiliations

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

Back to tab navigation

Supplementary files

Publication details

The article was received on 10 Oct 2018, accepted on 21 Dec 2018 and first published on 21 Dec 2018


Article type: Edge Article
DOI: 10.1039/C8SC04516J
Citation: Chem. Sci., 2019,10, 2298-2307
  • Open access: Creative Commons BY license
  •   Request permissions

    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. Material from this article can be used in other publications provided that the correct acknowledgement is given with the reproduced material.

    Reproduced material should be attributed as follows:

    • For reproduction of material from NJC:
      [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the Centre National de la Recherche Scientifique (CNRS) and the RSC.
    • For reproduction of material from PCCP:
      [Original citation] - Published by the PCCP Owner Societies.
    • For reproduction of material from PPS:
      [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the European Society for Photobiology, the European Photochemistry Association, and RSC.
    • For reproduction of material from all other RSC journals:
      [Original citation] - Published by The Royal Society of Chemistry.

    Information about reproducing material from RSC articles with different licences is available on our Permission Requests page.

Search articles by author

Spotlight

Advertisements