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 artifcial intelligence approach (AI) subgroup discovery. We identify catalyst \textit{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 frst-principles data for a broad family of oxides. We demonstrate that surfaces of experimentally identifed good catalysts consistently exhibit combinations of \textit{genes} resulting in a strong elongation of a C-O bond. The same combinations of \textit{genes} also minimize the OCO-angle, the previously proposed indicator of activation, albeit under the constraint that the Sabatier principle is satisfed. Based on these fndings, we propose a set of new promising catalyst materials for CO2 conversion.
@article{arxiv.1912.06515,
title = {Artifcial-intelligence-driven discovery of catalyst \textit{genes} with application to CO2 activation on semiconductor oxides},
author = {Aliaksei Mazheika and Yanggang Wang and Rosendo Valero and Luca M. Ghiringhelli and Francesc Vines and Francesc Illas and Sergey V. Levchenko and Matthias Scheffler},
journal= {arXiv preprint arXiv:1912.06515},
year = {2022}
}