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

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 desirab...

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Autores principales: Price, Christopher C., Singh, Akash, Frey, Nathan C., Shenoy, Vivek B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9683700/
https://www.ncbi.nlm.nih.gov/pubmed/36417537
http://dx.doi.org/10.1126/sciadv.abq5944
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author Price, Christopher C.
Singh, Akash
Frey, Nathan C.
Shenoy, Vivek B.
author_facet Price, Christopher C.
Singh, Akash
Frey, Nathan C.
Shenoy, Vivek B.
author_sort Price, Christopher C.
collection PubMed
description 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.
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spelling pubmed-96837002022-12-05 Efficient catalyst screening using graph neural networks to predict strain effects on adsorption energy Price, Christopher C. Singh, Akash Frey, Nathan C. Shenoy, Vivek B. Sci Adv Physical and Materials Sciences 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. American Association for the Advancement of Science 2022-11-23 /pmc/articles/PMC9683700/ /pubmed/36417537 http://dx.doi.org/10.1126/sciadv.abq5944 Text en Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Physical and Materials Sciences
Price, Christopher C.
Singh, Akash
Frey, Nathan C.
Shenoy, Vivek B.
Efficient catalyst screening using graph neural networks to predict strain effects on adsorption energy
title Efficient catalyst screening using graph neural networks to predict strain effects on adsorption energy
title_full Efficient catalyst screening using graph neural networks to predict strain effects on adsorption energy
title_fullStr Efficient catalyst screening using graph neural networks to predict strain effects on adsorption energy
title_full_unstemmed Efficient catalyst screening using graph neural networks to predict strain effects on adsorption energy
title_short Efficient catalyst screening using graph neural networks to predict strain effects on adsorption energy
title_sort efficient catalyst screening using graph neural networks to predict strain effects on adsorption energy
topic Physical and Materials Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9683700/
https://www.ncbi.nlm.nih.gov/pubmed/36417537
http://dx.doi.org/10.1126/sciadv.abq5944
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