Details

Autor(en) / Beteiligte

• Stocker, Sina
• Csányi, Gábor
• Reuter, Karsten
• Margraf, Johannes T.

Titel

Machine learning in chemical reaction space

Ist Teil von

• Nature communications, 2020-10, Vol.11 (1), p.5505-5505, Article 5505

Ort / Verlag

London: Nature Publishing Group

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Abstract Chemical compound space refers to the vast set of all possible chemical compounds, estimated to contain 10 60 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.