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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 unseen test data, outperforming ensemble linear baselines. Using ammonia synthesis as an example, we identify Cu-S alloy catalysts as promising candidates for strain engineering. Our approach can locate strain patterns that break adsorption energy scaling relations to improve catalyst performance.

SUBMITTER: Price CC

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

REPOSITORIES: biostudies-literature

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

Price Christopher C CC Singh Akash A Frey Nathan C NC Shenoy Vivek B VB

Science advances 20221123 47

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/ ...[more]

PMID: 36417537

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Dataset Information

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

Publications

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

Similar Datasets

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