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Area of Science:

  • Computational chemistry
  • Materials science
  • Machine learning

Background:

  • Machine learning interatomic potentials (MLIPs) often decompose predictions into body-ordered energy contributions.
  • The inherent "effective body-orderedness" and its influence on MLIP accuracy remain poorly understood.

Purpose of the Study:

  • To investigate how MLIPs decompose energy into body-ordered contributions.
  • To understand the factors influencing MLIPs' effective body-orderedness.
  • To explore the impact of body-orderedness on MLIP accuracy and learning behavior.

Main Methods:

  • Discussed challenges in applying many-body expansion to ab initio calculations.
  • Trained various MLIPs on hydrogen cluster datasets.
  • Analyzed emergent body-order trends and model generalizability.

Main Results:

  • MLIPs exhibit an inherent tendency to deduce effective body-order trends.
  • These trends are dependent on the ML model type and dataset composition.
  • Observed varying convergence and generalizability based on body-order trends.

Conclusions:

  • MLIPs self-determine their body-ordered energy decomposition.
  • Understanding these emergent trends is crucial for developing more accurate and generalizable MLIPs.
  • Provides insights for future MLIP development and application.