Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis

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 surface atomic configurations. To address this challenge, we present the Adsorbate Chemical Environment-based Graph Convolution Neural Network (ACE-GCN), a screening workflow that can account 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 Pt3Sn(111) alloy surface, of interest for nitrate electroreduction processes, where high adsorbate coverages combine with the low symmetry of the alloy substrate to produce a large configurational space, and (ii) OH* adsorbed on a stepped Pt(221) facet, of relevance to the Oxygen Reduction Reaction, wherein the presence of irregular crystal surfaces, high adsorbate coverages, and directionally-dependent adsorbate-adsorbate interactions result in the configurational complexity. In both cases, the ACE-GCN model, having trained on a fraction (~10%) of the total DFT-relaxed configurations, successfully ranks 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.

Identifier
Source https://archive.materialscloud.org/record/2022.50
Metadata Access https://archive.materialscloud.org/xml?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:1306
Provenance
Creator Ghanekar, Pushkar; Deshpande, Siddharth; Greeley, Jeffrey
Publisher Materials Cloud
Publication Year 2022
Rights info:eu-repo/semantics/openAccess; MIT License https://spdx.org/licenses/MIT.html
OpenAccess true
Contact archive(at)materialscloud.org
Representation
Language English
Resource Type Dataset
Discipline Materials Science and Engineering