Insights into Supported Subnanometer Catalysts Exposed to CO via Machine-Learning-Enabled Multiscale Modeling

Wang, Yifan; Su, Ya-Qiong; Hensen, Emiel M.; Vlachos, Dionisios G.

doi:10.1021/acs.chemmater.1c03616

Title: Insights into Supported Subnanometer Catalysts Exposed to CO via Machine-Learning-Enabled Multiscale Modeling

Journal Article · 02 February 2022 · Chemistry of Materials

DOI: https://doi.org/10.1021/acs.chemmater.1c03616 · OSTI ID:2212393

Wang, Yifan ^[1];

^[2];

^[3];

^[4]

University of Delaware, Newark, DE (United States); University of Delaware
Xi’an Jiaotong University (China); Eindhoven University of Technology (The Netherland)
Eindhoven University of Technology (The Netherlands)
University of Delaware, Newark, DE (United States)

Subnanometer catalysts offer high noble metal utilization and superior performance for several reactions. However, understanding their structures and properties on an atomic scale under working conditions is challenging due to the large configurational space. Here, we introduce an efficient multiscale framework to predict their stability exposed to an adsorbate. The framework integrates a comprehensive toolset including density functional theory (DFT) calculations, cluster expansion, machine learning, and structure optimization. The end-to-end machine-learning workflow guides DFT data generation and enables significant computational acceleration. We demonstrate the approach for CO-adsorbed Pdn (n = 1–55) clusters on CeO₂(111). Simulation results reveal that CO can facilitate restructuring by stabilizing smaller planar structures and bilayer structures of specific intermediate sizes, consistent with experimental reports. Metal–support interactions, preferential CO adsorption, and metal nuclearity and structure control catalyst stability. As a result, the framework allows automatic discovery of stable catalyst structures and a systematic strategy to exploit properties in the subnanometer scale.

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Research Organization:: RAPID Manufacturing Institute, Newark, DE (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Materials & Manufacturing Technologies Office (AMMTO)

Grant/Contract Number:: EE0007888; SC0001004

OSTI ID:: 2212393

Journal Information:: Chemistry of Materials, Journal Name: Chemistry of Materials Journal Issue: 4 Vol. 34; ISSN 0897-4756

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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