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  3. Machine-learned Electrostatic Potentials For Accurate Hydration Free Energy Calculations.
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  3. Machine-learned Electrostatic Potentials For Accurate Hydration Free Energy Calculations.

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Machine-Learned Electrostatic Potentials for Accurate Hydration Free Energy Calculations.

Mathias Hilfiker1,2, Leonardo Medrano Sandonas3, Alexandre Tkatchenko1

  • 1Department of Physics and Materials Science, University of Luxembourg, Luxembourg City L-1511, Luxembourg.

Journal of Chemical Theory and Computation
|April 20, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Accurate partial charges improve free energy calculations. This study introduces a machine learning model to predict accurate charges, enhancing molecular dynamics simulations and reducing errors in hydration free energy predictions.

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Area of Science:

  • Computational Chemistry
  • Molecular Modeling
  • Physical Chemistry

Background:

  • Free energy calculations are crucial in computational chemistry but limited by partial charge accuracy.
  • Semiempirical methods like AM1-BCC often yield inaccurate charges for polar species, impacting hydration free energy predictions.
  • Existing methods struggle with charge assignment reproducibility and accuracy for diverse molecular conformations.

Purpose of the Study:

  • To establish the link between inaccurate AM1-BCC charges and poor hydration free energy calculations.
  • To develop a rapid and accurate method for predicting partial charges using machine learning.
  • To introduce a novel method for assigning ensemble-averaged charges and improve free energy calculations.

Main Methods:

  • Utilized an XGBoost regressor trained on atomic descriptors to predict charges from high-fidelity DFT calculations (PBE0-D3(BJ)/def2-TZVP).
  • Developed the Boltzmann Percentile method combining the predictive model with molecular dynamics simulations for ensemble charge assignment.
  • Calculated hydration free energies on the FreeSolv dataset using both traditional and the new charge assignment methods.

    • Main Results:

    • The XGBoost model accurately predicts DFT-level charges, improving electrostatic descriptions.
    • The Boltzmann Percentile method yields charges robust to conformational variations.
    • Achieved a root mean squared error of 1.69 kcal/mol for hydration free energies, significantly outperforming AM1-BCC (3.05 kcal/mol).

    Conclusions:

    • The proposed machine learning approach and Boltzmann Percentile method offer a realistic enhancement for free energy calculations.
    • This method provides accurate and reproducible charge assignments at a computational cost comparable to semiempirical methods.
    • Enables more reliable molecular dynamics simulations in condensed phases by improving charge accuracy.