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Media type:
E-Article
Title:
Machine learning in chemical reaction space
Contributor:
Stocker, Sina;
Csányi, Gábor;
Reuter, Karsten;
Margraf, Johannes T.
Published:
Springer Science and Business Media LLC, 2020
Published in:
Nature Communications, 11 (2020) 1
Language:
English
DOI:
10.1038/s41467-020-19267-x
ISSN:
2041-1723
Origination:
Footnote:
Description:
<jats:title>Abstract</jats:title><jats:p>Chemical compound space refers to the vast set of all possible chemical compounds, estimated to contain 10<jats:sup>60</jats:sup> 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.</jats:p>