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Title: Artificial-intelligence-driven discovery of catalyst genes with application to CO2 activation on semiconductor oxides
Author: Mazheika, Aliaksei
Wang, Yang-Gang
Valero Montero, Rosendo
Viñes Solana, Francesc
Illas i Riera, Francesc
Ghiringelli, Luca M.
Levchenko, Sergey V.
Scheffler, Matthias
Keywords: Espectroscòpia infraroja
Teoria del funcional de densitat
Infrared spectroscopy
Density functionals
Issue Date: 20-Jan-2022
Publisher: Nature Publishing Group
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.
Note: Reproducció del document publicat a:
It is part of: Nature Communications, 2022, vol. 13, p. 419
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ISSN: 2041-1723
Appears in Collections:Articles publicats en revistes (Ciència dels Materials i Química Física)
Articles publicats en revistes (Institut de Química Teòrica i Computacional (IQTCUB))

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