Artificial-intelligence-driven discovery of catalyst genes with application to CO2 activation on semiconductor oxides

Aliaksei Mazheika (), Yang-Gang Wang, Rosendo Valero, Francesc Viñes, Francesc Illas, Luca M. Ghiringhelli, Sergey V. Levchenko () and Matthias Scheffler
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Aliaksei Mazheika: The NOMAD Laboratory at the Fritz-Haber-Institut der Max-Planck-Gesellschaft
Yang-Gang Wang: The NOMAD Laboratory at the Fritz-Haber-Institut der Max-Planck-Gesellschaft
Rosendo Valero: Universitat de Barcelona
Francesc Viñes: Universitat de Barcelona
Francesc Illas: Universitat de Barcelona
Luca M. Ghiringhelli: The NOMAD Laboratory at the Fritz-Haber-Institut der Max-Planck-Gesellschaft
Sergey V. Levchenko: Skolkovo Innovation Center
Matthias Scheffler: The NOMAD Laboratory at the Fritz-Haber-Institut der Max-Planck-Gesellschaft

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: Abstract Catalytic-materials design requires predictive modeling of the interaction between catalyst and reactants. This is challenging due to the complexity and diversity of structure-property relationships across the chemical space. Here, we report a strategy for a rational design of catalytic materials using the artificial intelligence approach (AI) subgroup discovery. We identify catalyst genes (features) that correlate with mechanisms that trigger, facilitate, or hinder the activation of carbon dioxide (CO2) towards a chemical conversion. The AI model is trained on first-principles data for a broad family of oxides. We demonstrate that surfaces of experimentally identified good catalysts consistently exhibit combinations of genes resulting in a strong elongation of a C-O bond. The same combinations of genes also minimize the OCO-angle, the previously proposed indicator of activation, albeit under the constraint that the Sabatier principle is satisfied. Based on these findings, we propose a set of new promising catalyst materials for CO2 conversion.

Date: 2022
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DOI: 10.1038/s41467-022-28042-z

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