Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis
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"metadata": {
"edited_by": 576,
"owner": 714,
"description": "Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale factors, ranging from the catalysts\u2019 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.",
"keywords": [
"density-functional theory",
"machine learning",
"electronic structure"
],
"is_last": true,
"title": "Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis",
"status": "published",
"license_addendum": null,
"doi": "10.24435/materialscloud:td-hf",
"conceptrecid": "1305",
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"references": [
{
"citation": "Ghanekar P, Deshpande S, Greeley J. Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis. ChemRxiv. Cambridge: Cambridge Open Engage; 2021",
"comment": "Preprint version of the manuscript where the data is discussed",
"url": "https://chemrxiv.org/engage/chemrxiv/article-details/60f5d5bf7bf0c92ab45f6c29",
"doi": "10.26434/chemrxiv-2021-8fcxm",
"type": "Preprint"
}
],
"contributors": [
{
"givennames": "Pushkar",
"affiliations": [
"Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907, USA"
],
"familyname": "Ghanekar",
"email": "pghaneka@purdue.edu"
},
{
"givennames": "Siddharth",
"affiliations": [
"Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907, USA",
"Department of Chemical Engineering, University of Delaware, Newark, DE"
],
"familyname": "Deshpande",
"email": "sdeshpan@udel.edu"
},
{
"givennames": "Jeffrey",
"affiliations": [
"Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907, USA"
],
"familyname": "Greeley",
"email": "jgreeley@purdue.edu"
}
],
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"publication_date": "Apr 11, 2022, 14:37:43",
"mcid": "2022.50",
"version": 1,
"id": "1306",
"license": "MIT License"
},
"revision": 4,
"created": "2022-04-03T21:06:39.671159+00:00",
"id": "1306",
"updated": "2022-04-11T12:37:43.529364+00:00"
}