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Title: Finite Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization

Abstract

Single-atom catalysts (SACs) minimize noble metal utilization and can alter the activity and selectivity of supported metal nanoparticles. However, the morphology of active centers, including single atoms and subnanometer clusters of a few atoms, remains elusive due to experimental challenges. The computational cost to describe numerous cluster shapes and sizes makes direct first-principles calculations impractical. We present a computational framework to enable structure determination for single-atom and subnanometer cluster catalysts. As a case study, we obtained the low energy structures of Pdn (n = 1-21) clusters supported on CeO2(111), which are critical components of automobile three-way catalysts. Trained on density functional theory data, a three-dimensional cluster expansion is established using statistical learning to describe the Hamiltonian and predict energies of supported Pdn clusters of any structure. Low energy stable and metastable structures are identified using a Metropolis Monte Carlo-based genetic algorithm in the canonical ensemble at 300 K. We observe that supported single atoms sinter to form bilayer clusters and large cluster isomers share similarities in both shape and energy, and elucidate the significance of the support and microstructure on cluster stability. We discovered a simple surrogate structure-energy model, where the energy per atom scales with the square root ofmore » the average first coordination number, which can be used to estimate energies and compare the stability of clusters. Our framework, applicable to any metal/support system, fills an important methodological gap to predict the stability of supported metal catalysts in the subnanometer regime.« less

Authors:
 [1];  [2];  [3];  [1]
  1. Univ. of Delaware, Newark, DE (United States). Catalysis Center for Energy Innovation, RAPID Manufacturing Inst., and Delaware Energy Inst.
  2. Eindhoven Univ. of Technology (Netherlands). Lab. of Inorganic Materials and Catalysis; Xi'an Jiaotong Univ., Shaanxi (China). Xi'an Key Laboratory of Sustainable Energy Materials Chemistry, MOE Key Lab. for Nonequilibrium Synthesis and Modulation of Condensed Matter, State Key Lab. of Electrical Insulation and Power Equipment
  3. Eindhoven Univ. of Technology (Netherlands). Lab. of Inorganic Materials and Catalysis
Publication Date:
Research Org.:
Univ. of Delaware, Newark, DE (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Science (SC), Basic Energy Sciences (BES); Netherlands Organization for Scientific Research (NWO); European Union (EU)
Contributing Org.:
Eindhoven University of Technology
OSTI Identifier:
1670851
Grant/Contract Number:  
EE0007888; SC0001004; 686086
Resource Type:
Accepted Manuscript
Journal Name:
ACS Nano
Additional Journal Information:
Journal Name: ACS Nano; Journal ID: ISSN 1936--0851
Country of Publication:
United States
Language:
English
Subject:
Single-atom catalysis; subnanometer catalysis; cluster expansion; genetic algorithm; catalyst structure

Citation Formats

Wang, Yifan, Su, Ya-qiong, Hensen, Emiel, and Vlachos, Dion. Finite Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization. United States: N. p., 2020. Web. doi:10.1021/acsnano.0c06472.
Wang, Yifan, Su, Ya-qiong, Hensen, Emiel, & Vlachos, Dion. Finite Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization. United States. doi:10.1021/acsnano.0c06472.
Wang, Yifan, Su, Ya-qiong, Hensen, Emiel, and Vlachos, Dion. Wed . "Finite Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization". United States. doi:10.1021/acsnano.0c06472.
@article{osti_1670851,
title = {Finite Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization},
author = {Wang, Yifan and Su, Ya-qiong and Hensen, Emiel and Vlachos, Dion},
abstractNote = {Single-atom catalysts (SACs) minimize noble metal utilization and can alter the activity and selectivity of supported metal nanoparticles. However, the morphology of active centers, including single atoms and subnanometer clusters of a few atoms, remains elusive due to experimental challenges. The computational cost to describe numerous cluster shapes and sizes makes direct first-principles calculations impractical. We present a computational framework to enable structure determination for single-atom and subnanometer cluster catalysts. As a case study, we obtained the low energy structures of Pdn (n = 1-21) clusters supported on CeO2(111), which are critical components of automobile three-way catalysts. Trained on density functional theory data, a three-dimensional cluster expansion is established using statistical learning to describe the Hamiltonian and predict energies of supported Pdn clusters of any structure. Low energy stable and metastable structures are identified using a Metropolis Monte Carlo-based genetic algorithm in the canonical ensemble at 300 K. We observe that supported single atoms sinter to form bilayer clusters and large cluster isomers share similarities in both shape and energy, and elucidate the significance of the support and microstructure on cluster stability. We discovered a simple surrogate structure-energy model, where the energy per atom scales with the square root of the average first coordination number, which can be used to estimate energies and compare the stability of clusters. Our framework, applicable to any metal/support system, fills an important methodological gap to predict the stability of supported metal catalysts in the subnanometer regime.},
doi = {10.1021/acsnano.0c06472},
journal = {ACS Nano},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {10}
}

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