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Updated: Jun 7, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Machine-learning surrogate models for particle insertions and element substitutions.

Ryosuke Jinnouchi1

  • 1Toyota Central R&D Labs., Inc., 41-1 Yokomichi, Nagakute, Aichi 480-1192, Japan.

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Summary

Two new machine learning methods accurately calculate chemical potentials for atoms and molecules in liquids. These approaches, particle insertion and element substitution, offer reproducible and precise results validated by experimental data.

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Area of Science:

  • Computational Chemistry
  • Physical Chemistry
  • Machine Learning in Science

Background:

  • Accurate computation of chemical potentials is crucial for understanding chemical reactions and material properties.
  • Traditional methods can be computationally expensive and challenging for complex systems.

Purpose of the Study:

  • To develop and compare two machine learning-aided thermodynamic integration schemes for computing chemical potentials.
  • To validate the accuracy and reproducibility of these novel methods.

Main Methods:

  • Developed two distinct thermodynamic integration schemes: particle insertion and combined particle insertion-element substitution.
  • Employed machine-learned potentials trained on first-principles datasets.
  • Corrected machine learning model errors by integrating back to first-principles potentials.

Main Results:

  • Both methods yielded identical real potentials for proton, alkali metal cations, and halide anions in water within statistical error.
  • Computed real potentials and solvation structures showed good agreement with experimental and simulation data.
  • Demonstrated the reproducibility of the calculated real potentials.

Conclusions:

  • Machine learning surrogate models offer a precise and reproducible approach for determining atomic and molecular chemical potentials.
  • The developed methods, particle insertion and element substitution, provide reliable pathways for chemical potential calculations.
  • These findings advance the application of machine learning in computational chemistry.