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  3. Constant-potential Machine Learning Force Field For The Electrochemical Interface.
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Constant-Potential Machine Learning Force Field for the Electrochemical Interface.

Ruoyu Wang1, Shaoheng Fang2, Qixing Huang2

  • 1Texas Materials Institute and Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States.

Journal of Chemical Theory and Computation
|July 28, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

We developed a constant potential machine learning force field (CP-MLFF) for accurate atomistic simulations of electrochemical interfaces. This new method enables efficient large-scale modeling of electrode potential effects in catalysis.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Electrochemistry

Background:

  • Accurate prediction of electrochemical interfaces necessitates large-scale atomistic simulations.
  • Machine learning force fields (MLFFs) offer an effective simulation approach.
  • Existing MLFFs often neglect electrode potential effects, limiting their applicability.

Purpose of the Study:

  • To develop a novel constant potential MLFF (CP-MLFF) capable of incorporating electrode potential.
  • To enable grand canonical ensemble simulations for interface electrons.
  • To provide a tool for efficient and accurate large-scale simulations of electrochemical interfaces.

Main Methods:

  • Developed a CP-MLFF using an equivariant graph neural network architecture.
  • Integrated the CP-MLFF into the MACE framework.
  • Designed the architecture to accept electron count as input for Fermi level prediction.

    • Main Results:

    • The CP-MLFF accurately predicts the Fermi level.
    • Demonstrated the ability to study the convergency of electrochemical barriers with respect to sampling.
    • Applied the CP-MLFF to simulate CO2 reduction on a Ni-N-C catalyst.

    Conclusions:

    • The developed CP-MLFF is a valuable tool for simulating electrochemical interfaces.
    • This method facilitates accurate and efficient large-scale atomistic simulations.
    • Enables deeper understanding and prediction of electrochemical interface phenomena.