4.8 Article

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

期刊

CHEMISTRY OF MATERIALS
卷 34, 期 4, 页码 1611-1619

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acs.chemmater.1c03616

关键词

-

资金

  1. Catalysis Center for Energy Innovation, an Energy Frontier Research Center - U.S. Department of Energy, Office of Science, and Office of Basic Energy Sciences [DE-SC0001004]
  2. Department of Energy's Office of Energy Efficient and Renewable Energy's Advanced Manufacturing Office [DE-EE0007888-9.5]
  3. State of Delaware toward the RAPID projects
  4. Netherlands Organization for Scientific Research (NWO) through a Vici grant
  5. European Union [686086]
  6. Young Talent Support Plan Fellowship of Xi'an Jiaotong University

向作者/读者索取更多资源

Subnanometer catalysts provide high noble metal utilization and superior performance for various reactions. However, understanding their atomic-scale structures and properties under working conditions is challenging due to the large configurational space. In this study, an efficient multiscale framework integrating density functional theory (DFT) calculations, cluster expansion, machine learning, and structure optimization is introduced to predict the stability of catalysts exposed to adsorbates. The framework enables automatic discovery of stable catalyst structures and a systematic strategy to exploit properties at the subnanometer scale. Simulation results for CO-adsorbed Pd-n (n = 1-55) clusters on CeO2(111) reveal that CO can facilitate restructuring by stabilizing smaller planar structures and bilayer structures of specific intermediate sizes.
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 Pd-n (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. The framework allows automatic discovery of stable catalyst structures and a systematic strategy to exploit properties in the subnanometer scale.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据