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<p>Developing truly universal machine learning potentials for heterogeneous catalysis remains challenging. Here we introduce our element-based machine learning potential (EMLP), trained on a unique random exploration via imaginary chemicals optimization (REICO) sampling strategy. REICO samples diverse local atomic environments to build a representative dataset of atomic interactions, making the EMLP inherently general and reactive, capable of accurately predicting elementary reactions without explicit structural or reaction pathway inputs. We demonstrate the generality and reactivity of our approach by building a Ag-Pd-C-H-O EMLP targeting Pd–Ag catalysts interacting with C/H/O-containing species, achieving quantitative agreement with density functional theory even for complex scenarios such as surface reconstruction, coverage effects and solvent environments, cases for which existing foundation models typically fail. Our method paves the way to replace density functional theory calculations for large and intricate systems in heterogeneous catalysis, and offers a general framework that can readily be extended to other catalytic systems, and to broader fields such as materials science.</p><p></p>

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General reactive element-based machine learning potentials for heterogeneous catalysis

  • 期刊论文

  • 发表日期：

    2025年9月23日

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  • Changxi Yang,
  • Chenyu Wu,
  • Wenbo Xie,
  • Daiqian Xie,
  • P. Hu

摘要

Developing truly universal machine learning potentials for heterogeneous catalysis remains challenging. Here we introduce our element-based machine learning potential (EMLP), trained on a unique random exploration via imaginary chemicals optimization (REICO) sampling strategy. REICO samples diverse local atomic environments to build a representative dataset of atomic interactions, making the EMLP inherently general and reactive, capable of accurately predicting elementary reactions without explicit structural or reaction pathway inputs. We demonstrate the generality and reactivity of our approach by building a Ag-Pd-C-H-O EMLP targeting Pd–Ag catalysts interacting with C/H/O-containing species, achieving quantitative agreement with density functional theory even for complex scenarios such as surface reconstruction, coverage effects and solvent environments, cases for which existing foundation models typically fail. Our method paves the way to replace density functional theory calculations for large and intricate systems in heterogeneous catalysis, and offers a general framework that can readily be extended to other catalytic systems, and to broader fields such as materials science.