The excessive emissions of carbon dioxide (CO$_2$) into the atmosphere
threaten to shift the CO$_2$ cycle planet-wide and induce unpredictable climate
changes. Using artificial intelligence (AI) trained on high-throughput first
principles based data for a broad family of oxides, we develop a strategy for a
rational design of catalytic materials for converting CO$_2$ to fuels and other
useful chemicals. We demonstrate that an electron transfer to the
$\pi^*$-antibonding orbital of the adsorbed molecule and the associated bending
of the initially linear molecule, previously proposed as the indicator of
activation, are insufficient to account for the good catalytic performance of
experimentally characterized oxide surfaces. Instead, our AI model identifies
the common feature of these surfaces in the binding of a molecular O atom to a
surface cation, which results in a strong elongation and therefore weakening of
one molecular C-O bond. This finding suggests using the C-O bond elongation as
an indicator of CO$_2$ activation. Based on these findings, we propose a set of
new promising oxide-based catalysts for CO$_2$ conversion, and a recipe to find
more

Ghiringhelli, Luca M.

Illas, Francesc

Levchenko, Sergey V.

Mazheika, Aliaksei

Scheffler, Matthias

Valero, Rosendo

Vines, Francesc

Wang, Yanggang

Nature Communications

English

arXiv

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

Mazheika, A.

Wang, Y.

Valero, R.

Viñes, F.

Illas, F.

Ghiringhelli, L.

Levchenko, S.

Scheffler, M.

MPG.PuRe

Artificial-intelligence-driven discovery of catalyst genes with application to CO<sub>2</sub> activation on semiconductor oxides

Wang, Yang-Gang

Valero Montero, Rosendo

Viñes Solana, Francesc

Illas i Riera, Francesc

Ghiringelli, Luca M.

Diposit Digital de la Universitat de Barcelona

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

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

arXiv.org e-Print Archive

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

http://dx.doi.org/10.1038/s41467-022-28042-z