Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Ghanekar, Pushkar</dc:creator>
  <dc:creator>Deshpande, Siddharth</dc:creator>
  <dc:creator>Greeley, Jeffrey</dc:creator>
  <dc:date>2022-04-11</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://archive.materialscloud.org/record/2022.50</dc:identifier>
  <dc:identifier>doi:10.24435/materialscloud:td-hf</dc:identifier>
  <dc:identifier>mcid:2022.50</dc:identifier>
  <dc:identifier>oai:materialscloud.org:1306</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Materials Cloud</dc:publisher>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>MIT License https://spdx.org/licenses/MIT.html</dc:rights>
  <dc:subject>density-functional theory</dc:subject>
  <dc:subject>machine learning</dc:subject>
  <dc:subject>electronic structure</dc:subject>
  <dc:title>Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis</dc:title>
  <dc:type>Dataset</dc:type>
</oai_dc:dc>