Machine-Learned Potentials for Solvation Modeling

TL;DR

This paper reviews the development and application of machine-learned potentials (MLPs) for solvation modeling, highlighting their ability to efficiently and accurately simulate solvated systems and discussing future challenges.

Contribution

It provides a comprehensive overview of MLPs in solvation modeling, including theoretical foundations, classification, integration, and future directions.

Findings

MLPs enable efficient, first-principles accuracy in solvation simulations.

Case studies demonstrate MLPs' effectiveness across various systems.

Discussion of open challenges guides future research in transferable MLPs.

Abstract

Solvent environments play a central role in determining molecular structure, energetics, reactivity, and interfacial phenomena. However, modeling solvation from first principles remains difficult due to the complex interplay of interactions and unfavorable computational scaling of first-principles treatment with system size. Machine-learned potentials (MLPs) have recently emerged as efficient surrogates for quantum chemistry methods, offering first-principles accuracy at greatly reduced computational cost. MLPs approximate the underlying potential energy surface, enabling efficient computation of energies and forces in solvated systems, and are capable of accounting for effects such as hydrogen bonding, long-range polarization, and conformational changes. This review surveys the development and application of MLPs in solvation modeling. We summarize the theoretical basis of MLP-based energy and force predictions and present a classification of MLPs based on training targets, model types, and design choices related to architectures, descriptors, and training protocols. Integration into established solvation workflows is discussed, with case studies spanning small molecules, interfaces, and reactive systems. We conclude by outlining open challenges and future directions toward transferable, robust, and physically grounded MLPs for solvation-aware atomistic modeling.