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Machine learning in chemical reaction space

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Reuter,  Karsten
Chair of Theoretical Chemistry and Catalysis Research Center, Technische Universität München;
Theory, Fritz Haber Institute, Max Planck Society;

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Citation

Stocker, S., Csányi, G., Reuter, K., & Margaf, J. T. (2020). Machine learning in chemical reaction space. Nature Communications, 11: 5505 (2020). doi:10.1038/s41467-020-19267-x.


Cite as: http://hdl.handle.net/21.11116/0000-0007-5B1C-5
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.