Artifcial-intelligence-driven discovery of catalyst \textit{genes} with application to CO2 activation on semiconductor oxides

TL;DR

This paper presents an AI-driven approach to identify catalyst features that influence CO2 activation on semiconductor oxides, enabling rational design of new catalysts based on first-principles data.

Contribution

The study introduces a novel AI subgroup discovery method to find catalyst genes correlated with CO2 activation, advancing materials design.

Findings

Identified catalyst genes linked to CO2 activation mechanisms.

Validated that certain surface features lead to stronger C-O bonds.

Proposed new catalyst materials for CO2 conversion.

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 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 (CO$_2$) 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 CO$_2$ conversion.