Machine learning in chemical reaction space

Sina Stocker, Gábor Csányi, Karsten Reuter and Johannes T. Margraf ()
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Sina Stocker: Technische Universität München
Gábor Csányi: University of Cambridge
Karsten Reuter: Technische Universität München
Johannes T. Margraf: Technische Universität München

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: Abstract Chemical compound space refers to the vast set of all possible chemical compounds, estimated to contain 1060 molecules. While intractable as a whole, modern machine learning (ML) is increasingly capable of accurately predicting molecular properties in important subsets. Here, we therefore engage in the ML-driven study of even larger reaction space. Central to chemistry as a science of transformations, this space contains all possible chemical reactions. As an important basis for ‘reactive’ ML, we establish a first-principles database (Rad-6) containing closed and open-shell organic molecules, along with an associated database of chemical reaction energies (Rad-6-RE). We show that the special topology of reaction spaces, with central hub molecules involved in multiple reactions, requires a modification of existing compound space ML-concepts. Showcased by the application to methane combustion, we demonstrate that the learned reaction energies offer a non-empirical route to rationally extract reduced reaction networks for detailed microkinetic analyses.

Date: 2020
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DOI: 10.1038/s41467-020-19267-x

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