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Updated: Jul 23, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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Cross-platform hyperparameter optimization for machine learning interatomic potentials.

Daniel F Thomas du Toit1, Volker L Deringer1

  • 1Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford OX1 3QR, United Kingdom.

The Journal of Chemical Physics
|July 11, 2023
PubMed
Summary
This summary is machine-generated.

Optimizing hyperparameters is crucial for machine-learning (ML) potentials in material modeling. This work introduces an open-source Python package to streamline hyperparameter optimization for ML interatomic potentials.

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Area of Science:

  • Computational Materials Science
  • Physical Chemistry
  • Data Science

Background:

  • Machine-learning (ML) interatomic potentials are revolutionizing material modeling, enabling large-scale atomic simulations.
  • The accuracy of ML potentials is highly sensitive to hyperparameter selection, posing a significant challenge.
  • Many hyperparameters lack clear physical meaning, complicating optimization in vast search spaces.

Purpose of the Study:

  • To present an open-source Python package designed for efficient hyperparameter optimization in ML potential fitting.
  • To address the challenges associated with optimizing hyperparameters for ML interatomic potentials.
  • To facilitate the broader adoption of ML potentials in physical science research.

Main Methods:

  • Development of a versatile Python package for hyperparameter optimization.
  • Exploration of methodological considerations for optimization and validation data selection.
  • Demonstration of the package through example applications.

Main Results:

  • The package provides a unified approach to hyperparameter optimization across various ML potential frameworks.
  • Methodological insights into effective optimization strategies and validation data selection are discussed.
  • Example applications showcase the package's utility and effectiveness.

Conclusions:

  • The developed Python package simplifies and accelerates the critical process of hyperparameter optimization for ML potentials.
  • This tool is expected to integrate into larger computational frameworks, promoting wider use of ML potentials.
  • The work aims to reduce barriers to entry for researchers utilizing ML interatomic potentials in the physical sciences.