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The research aims to systematically identify and catalog active sites in bimetallic catalysts for CO2 reduction using machine learning methods.

July 27, 2017ACS CatalysisOpen Access

Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO₂ Reduction

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The research aims to systematically identify and catalog active sites in bimetallic catalysts for CO2 reduction using machine learning methods.
Developed a framework for systematic enumeration of active sites on bimetallic crystals.
Employed a neural-network-based surrogate model to reduce the number of density functional theory calculations.
Applied the process to nickel gallium bimetallics focusing on electrochemical CO2 reduction.
Identified hundreds of possible active sites on bimetallic facets.
Found that certain Ni sites with favorable configurations exhibit enhanced activity.
Demonstrated that predictive modeling can guide further experimental exploration on bimetallic catalysts.

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Randomized trial reveals effective active sites for CO2 reduction in bimetallic catalysts, suggesting new discovery pathways.

Ulissi et al. (2017) studied this question.

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