Regensburg 2022 – scientific programme

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O: Fachverband Oberflächenphysik

O 62: Surface Reactions and Heterogeneous Catalysis 1

O 62.6: Talk

Thursday, September 8, 2022, 11:45–12:00, H4

Finding catalyst genes with subgroup discovery — •Aliaksei Mazheika¹, Yanggang Wang², Rosendo Valero³, Francesc Vines³, Francesc Illas³, Luca M. Ghiringhelli⁴, Sergey V. Levchenko⁵, and Matthias Scheffler⁴ — ¹Technische Universitaet, Berlin, DE — ²University of Science and Technology, Shenzhen, CN — ³Universitat de Barcelona, Barcelona, ES — ⁴The NOMAD Laboratory at the Fritz Haber Institute and Humboldt University, Berlin, DE — ⁵Moscow

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.—A. Mazheika et. al. Nature Comm. 2022, 13, 419.