Efficient catalyst screening using graph neural networks to predict strain effects on adsorption energy

ABSTRACT: Small-molecule adsorption energies correlate with energy barriers of catalyzed intermediate reaction steps, determining the dominant microkinetic mechanism. Straining the catalyst can alter adsorption energies and break scaling relationships that inhibit reaction engineering, but identifying desirable strain patterns using density functional theory is intractable because of the high-dimensional search space. We train a graph neural network to predict the adsorption energy response of a catalyst/adsorbate system under a proposed surface strain pattern. The training data are generated by randomly straining and relaxing Cu-based binary alloy catalyst complexes taken from the Open Catalyst Project. The trained model successfully predicts the adsorption energy response for 85% of strains in uns

SUBMITTER: Price C 

PROVIDER: S-EPMC9683700 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature