Predicting Solvation Free Energies of Molecules and Ions via First-Principles and Machine-Learning Molecular Dynamics

TL;DR

This paper introduces a novel bubble method for calculating solvation free energies of molecules and ions from first principles, overcoming numerical instabilities in ab initio and machine learning molecular dynamics.

Contribution

The method avoids end-point singularities in simulations and includes corrections for periodic DFT calculations, applicable to arbitrary-shaped molecules and ions without empirical data.

Findings

Successfully computed SFEs for methane, methanol, water, and sodium ions.

Applicable to classical, ab initio, and machine learning MD simulations.

Requires no experimental inputs, suitable for extreme conditions.

Abstract

The solvation free energy (SFE) of molecules and ions is a fundamental property governing their solvation behavior and solubility. Molecular simulations offer a route to compute SFEs using alchemical free energy methods, such as thermodynamic integration or free energy perturbation. However, these methods suffer from the infamous end-point singularity, which leads to numerical instability when atoms approach closely, a challenge that becomes particularly acute in ab initio and machine learning molecular dynamics simulations. Here, we introduce the bubble method to calculate the SFEs of molecules and ions from first principles. Our approach avoids the end-state problem in both ab initio and machine learning molecular dynamics simulations and is applicable to molecules and ions of arbitrary shape. When calculating the SFEs of ions using periodic density functional theory, we incorporate corrections for the neutralizing background charge, spurious interactions between periodic images, and the vacuum-water interface potential. To validate our method, we successfully computed the SFEs of methane, methanol, water, and sodium ions using classical, ab initio, and machine learning molecular dynamics simulations. Importantly, our method requires no experimental inputs or empirical data. This makes it particularly well-suited for studying systems under extreme conditions, such as high pressure-temperature environments or under nanoconfinement, situations where experimental investigations are challenging and classical force fields, typically parameterized under ambient conditions, may be unreliable.