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Free keywords:
Condensed Matter, Materials Science, cond-mat.mtrl-sci, Physics, Chemical Physics, physics.chem-ph, Physics, Data Analysis, Statistics and Probability, physics.data-an
Abstract:
The excessive emissions of carbon dioxide (CO2) into the atmosphere
threaten to shift the CO2 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 CO2 to fuels and other useful chemicals. We demonstrate that an electron transfer to the π-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 CO2 activation. Based on these findings, we propose a set of new promising oxide-based catalysts for CO2 conversion, and a recipe to find more.
