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

Mazheika A, Wang YG, Valero R, Viñes F, Illas F, Ghiringhelli LM, Levchenko SV, Scheffler M (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Nature Communications Nature Publishing Group: Nature Communications

Book Volume: 13

Article Number: 419

Journal Issue: 1

DOI: 10.1038/s41467-022-28042-z

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.

Authors with CRIS profile

Luca Ghiringhelli

Involved external institutions

Fritz-Haber-Institut der Max-Planck-Gesellschaft (FHI) DE Germany (DE) Universitat de Barcelona (UB) / University of Barcelona ES Spain (ES) Skolkovo Institute of Science and Technology RU Russian Federation (RU)

How to cite

APA:

Mazheika, A., Wang, Y.G., Valero, R., Viñes, F., Illas, F., Ghiringhelli, L.M.,... Scheffler, M. (2022). Artificial-intelligence-driven discovery of catalyst genes with application to CO2 activation on semiconductor oxides. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-28042-z

MLA:

Mazheika, Aliaksei, et al. "Artificial-intelligence-driven discovery of catalyst genes with application to CO2 activation on semiconductor oxides." Nature Communications 13.1 (2022).

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