Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis

Ghanekar, Pushkar G.; Deshpande, Siddharth; Greeley, Jeffrey

doi:10.1038/s41467-022-33256-2

Title: Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis

Journal Article · 01 October 2022 · Nature Communications

DOI: https://doi.org/10.1038/s41467-022-33256-2 · OSTI ID:1890537

Ghanekar, Pushkar G.;

;

Abstract Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale factors, ranging from the catalysts’ local morphology to the presence of high adsorbate coverages. Describing such phenomena via computational models requires generation and analysis of a large space of atomic configurations. To address this challenge, we present Adsorbate Chemical Environment-based Graph Convolution Neural Network (ACE-GCN), a screening workflow that accounts for atomistic configurations comprising diverse adsorbates, binding locations, coordination environments, and substrate morphologies. Using this workflow, we develop catalyst surface models for two illustrative systems: (i) NO adsorbed on a Pt ₃ Sn(111) alloy surface, of interest for nitrate electroreduction processes, where high adsorbate coverages combined with low symmetry of the alloy substrate produce a large configurational space, and (ii) OH* adsorbed on a stepped Pt(221) facet, of relevance to the Oxygen Reduction Reaction, where configurational complexity results from the presence of irregular crystal surfaces, high adsorbate coverages, and directionally-dependent adsorbate-adsorbate interactions. In both cases, the ACE-GCN model, trained on a fraction (~10%) of the total DFT-relaxed configurations, successfully describes trends in the relative stabilities of unrelaxed atomic configurations sampled from a large configurational space. This approach is expected to accelerate development of rigorous descriptions of catalyst surfaces under in-situ conditions.

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Research Organization:: Argonne National Lab. (ANL), Argonne, IL (United States); Purdue Univ., West Lafayette, IN (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC02-06CH11357; SC0020381

OSTI ID:: 1890537

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 13; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
computational methods
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Title: Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis

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