Can DFT-Trained Neural Network Potentials Reproduce Structure, Solvation, and Water-Exchange Properties in Aqueous Magnesium Solutions?

Abstract

Abstract Magnesium ions play an essential role in many biological processes but remain challenging to model in biomolecular simulations. Despite considerable scientific effort, classical force fields fail to simultaneously reproduce key structural, thermodynamic, and kinetic solution properties, likely due to their inability to explicitly account for quantum many-body effects. Here, we develop and systematically benchmark MACE neural network potentials (NNPs) for aqueous MgCl2 solutions, trained on revPBE-D3/zd and revPBE0-D3/zd density functional theory reference data, and assess their ability to reproduce a broad range of experimental solution properties including the structure of the first hydration shell, diffusion coefficient, activity derivative, water-exchange rate, and mechanism as well as solvation free energy. Both NNPs accurately reproduce the octahedral structure of the first hydration shell, ion-pairing properties, and diffusion coefficients. Combining the NNPs with transition path sampling and other enhanced sampling techniques allows us to capture the rare event of water exchange in the first hydration shell of Mg2+, revealing a dissociative exchange mechanism. Transition interface sampling yields exchange rates within 1 order of magnitude of experiment, representing a substantial improvement over classical dissociative force fields. In contrast, the NNP-derived solvation free energy significantly underestimates the experimental value, revealing a limitation of the present local NNP architecture for describing ion solvation thermodynamics. Our results demonstrate that DFT-trained NNPs can accurately describe Mg2+ hydration structure, diffusion, ion pairing, and exchange kinetics while highlighting limitations of local NNP architectures and the importance of long-range electrostatic contributions to achieve quantitative agreement with experimental ion solvation free energies.

The authors' abstract, as published at the source. Journal of Chemical Theory and Computation, 2026 · DOI ↗