Publication date: Apr 11, 2022
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.
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README.txt
MD5md5:0569796192ae7ce5b25e5617981bb657
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5.6 KiB | File containing description of the data included in the folders and how to access it. |
Fig3.tar.bz2
MD5md5:fd61315613bb3e72b44d41319b4736b5
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84.4 KiB | Raw data to recreate analysis of Fig 3 |
Fig4.tar.bz2
MD5md5:8881a96a1113653b66b7affd3e32f58f
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28.2 KiB | Raw data to recreate analysis of Fig 4 |
Fig5.py
MD5md5:4bd13dcf4c7e02e336faed6ac6d05121
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2.0 KiB | File to generate Fig 5 |
processing_scripts.tar.bz2
MD5md5:18dcdabaef7de72c755db5fbe7c6b206
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11.5 KiB | processing script to generate graph objects |
Pt_OH.tar.bz2
MD5md5:a66bebea30fdd1a2cfdc6cc120456deb
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1.4 GiB | Raw files (graph objects and atom trajectories) for Pt-OH analysis |
Pt3Sn_NO.tar.bz2
MD5md5:26a89f4493cee403afaa77ba1cbc8eca
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378.6 MiB | Raw files (graph objects and atom trajectories) for Pt3Sn-NO analysis |
2022.50 (version v1) [This version] | Apr 11, 2022 | DOI10.24435/materialscloud:td-hf |