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Predicting Solvation Free Energies Using Electronegativity-Equalization Atomic Charges and a Dense Neural Network: A

Sergei F Vyboishchikov¹

¹Institut de Química Computacional i Catàlisi and Departament de Química, Universitat de Girona, Carrer Maria Aurèlia Capmany 69, 17003 Girona, Spain.

Journal of Chemical Theory and Computation

|November 14, 2023

Summary

A new dense Neural Network, ESE-GB-DNN, accurately predicts solvation free energies for molecules and ions. This method offers high efficiency and accuracy, rivaling standard DFT-based approaches for various solvents.

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Area of Science:

Computational Chemistry
Physical Chemistry
Machine Learning in Chemistry

Background:

Accurate prediction of solvation free energies (ΔG°solv) is crucial for understanding chemical processes.
Standard methods like DFT can be computationally intensive.
Developing efficient and accurate models for solvation free energy is an ongoing challenge.

Purpose of the Study:

To introduce ESE-GB-DNN, a novel dense Neural Network for evaluating solvation free energies.
To assess the accuracy and efficiency of ESE-GB-DNN across different solvent types and solute charges.
To compare the performance of ESE-GB-DNN against established DFT-based methods.

Main Methods:

Utilizing a dense Neural Network (ESE-GB-DNN) incorporating generalized-Born terms, atomic surface areas, and molecular volume as input features.
Employing a modified electronegativity-equalization method for calculating atomic charges.
Evaluating performance using root-mean-square error (RMSE) across various solvent classes (water, nonaqueous) and solute types (neutral molecules, ions).

Main Results:

ESE-GB-DNN achieves high accuracy for neutral solutes, with RMSEs ranging from 0.7 to 1.3 kcal/mol depending on the solvent.
The model demonstrates excellent performance for nonaqueous ion solutions, yielding an RMSE of approximately 0.7 kcal/mol.
For ions in water, ESE-GB-DNN shows a larger RMSE of 2.9 kcal/mol, indicating areas for further refinement.

Conclusions:

ESE-GB-DNN provides a simple, efficient, and highly accurate method for predicting solvation free energies.
The model's performance challenges that of standard DFT-based approaches, particularly for neutral solutes and nonaqueous ion solutions.
ESE-GB-DNN represents a significant advancement in computational chemistry for solvation free energy calculations.