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Hitchhikers Guide To Training More General Machine Learning Potentials in Heterogeneous Catalysis.

Chenyu Wu1,2, Changxi Yang2,3, Zhening Fang2

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Developing general machine learning potentials (MLPs) for catalysis requires diverse data, posing new training challenges. This study identifies pathologies in training with diverse data and offers guidance for robust MLP development.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Chemical Engineering

Background:

  • Machine learning potentials (MLPs) are advanced simulation tools for heterogeneous catalysis.
  • Current methods focus on system-specific applications, limiting generalizability.
  • Training general MLPs requires diverse, nonequilibrium data, presenting unique challenges.

Purpose of the Study:

  • To investigate challenges and pathologies in training general MLPs using diverse datasets.
  • To provide practical recommendations for developing transferable MLPs for catalysis.

Main Methods:

  • Systematic investigation of training pathologies using the REICO method for data generation.
  • Analysis of deviations from standard system-specific MLP training protocols.
  • Evaluation of data cleaning, model selection, and error metrics for diverse datasets.

Main Results:

  • Identified critical deviations and pathologies when training MLPs on diverse, nonequilibrium data.
  • Demonstrated challenges in standard training discipline and evaluation logic for general MLPs.
  • Highlighted the need for specialized approaches beyond system-specific workflows.

Conclusions:

  • Training general and transferable MLPs requires addressing unique challenges associated with diverse data.
  • Recommendations provided for data cleaning, model selection, and error metrics enhance MLP robustness.
  • Guidance offered for developing reliable MLPs applicable to a wider range of catalytic systems.