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Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis

Pushkar Ghanekar1*, Siddharth Deshpande1,2*, Jeffrey Greeley1*

1 Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47907, USA

2 Department of Chemical Engineering, University of Delaware, Newark, DE

* Corresponding authors emails: pghaneka@purdue.edu, sdeshpan@udel.edu, jgreeley@purdue.edu
DOI10.24435/materialscloud:td-hf [version v1]

Publication date: Apr 11, 2022

How to cite this record

Pushkar Ghanekar, Siddharth Deshpande, Jeffrey Greeley, Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis, Materials Cloud Archive 2022.50 (2022), doi: 10.24435/materialscloud:td-hf.

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.

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Files

File name Size Description
README.txt
MD5md5:0569796192ae7ce5b25e5617981bb657
5.6 KiB File containing description of the data included in the folders and how to access it.
Fig3.tar.bz2
MD5md5:fd61315613bb3e72b44d41319b4736b5
84.4 KiB Raw data to recreate analysis of Fig 3
Fig4.tar.bz2
MD5md5:8881a96a1113653b66b7affd3e32f58f
28.2 KiB Raw data to recreate analysis of Fig 4
Fig5.py
MD5md5:4bd13dcf4c7e02e336faed6ac6d05121
2.0 KiB File to generate Fig 5
processing_scripts.tar.bz2
MD5md5:18dcdabaef7de72c755db5fbe7c6b206
11.5 KiB processing script to generate graph objects
Pt_OH.tar.bz2
MD5md5:a66bebea30fdd1a2cfdc6cc120456deb
1.4 GiB Raw files (graph objects and atom trajectories) for Pt-OH analysis
Pt3Sn_NO.tar.bz2
MD5md5:26a89f4493cee403afaa77ba1cbc8eca
378.6 MiB Raw files (graph objects and atom trajectories) for Pt3Sn-NO analysis

License

Files and data are licensed under the terms of the following license: MIT License.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

Keywords

density-functional theory machine learning electronic structure

Version history:

2022.50 (version v1) [This version] Apr 11, 2022 DOI10.24435/materialscloud:td-hf