Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis.

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 Pt₃Sn(111) alloy surface, of interest for nitrate electroreduction processes, whe

SUBMITTER: Ghanekar PG 

PROVIDER: S-EPMC9527237 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature