Machine-Learned Potentials for Solvation Modeling

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.

revised, some reference updated

References in corpus (61)

• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• On representing chemical environments
• Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• SchNet - a deep learning architecture for molecules and materials
• DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
• E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
• Statistically optimal analysis of samples from multiple equilibrium states
• Moment Tensor Potentials: a class of systematically improvable interatomic potentials
• Quantum-Chemical Insights from Deep Tensor Neural Networks
• Machine Learning of Accurate Energy-Conserving Molecular Force Fields
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
• DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
• A Performance and Cost Assessment of Machine Learning Interatomic Potentials
• Machine learning for molecular simulation
• Big Data meets Quantum Chemistry Approximations: The $\Delta$-Machine Learning Approach
• Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
• Less is more: sampling chemical space with active learning
• DScribe: Library of Descriptors for Machine Learning in Materials Science
• On-the-fly machine learning force field generation: Application to melting points
• Nearsightedness of Electronic Matter
• A Fourth-Generation High-Dimensional Neural Network Potential with Accurate Electrostatics Including Non-local Charge Transfer
• Machine learning for electronically excited states of molecules
• Machine-learning interatomic potentials for materials science
• i-PI 2.0: A Universal Force Engine for Advanced Molecular Simulations
• The Phase Diagram of a Deep Potential Water Model
• SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
• FCHL revisited: faster and more accurate quantum machine learning
• ANI-1: A data set of 20M off-equilibrium DFT calculations for organic molecules
• Alchemical and structural distribution based representation for improved QML
• The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts
• Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network
• Hierarchical modeling of molecular energies using a deep neural network
• Unified Representation of Molecules and Crystals for Machine Learning
• sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
• Accurate Interatomic Force Fields via Machine Learning with Covariant Kernels
• Machine Learning for Quantum Mechanical Properties of Atoms in Molecules
• A learning scheme to predict atomic forces and accelerate materials simulations
• A practical guide to machine learning interatomic potentials -- Status and future
• Transfer learning for solvation free energies: from quantum chemistry to experiments
• Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science
• Committee neural network potentials control generalization errors and enable active learning
• QM7-X: A comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
• Strategies for the Construction of Machine-Learning Potentials for Accurate and Efficient Atomic-Scale Simulations
• Systematic Microsolvation Approach with a Cluster-Continuum Scheme and Conformational Sampling
• Tutorial: How to Train a Neural Network Potential
• Machine Learning of Free Energies in Chemical Compound Space Using Ensemble Representations: Reaching Experimental Uncertainty for Solvation
• Force-Field-Enhanced Neural Network Interactions: from Local Equivariant Embedding to Atom-in-Molecule properties and long-range effects
• High-dimensional neural network potentials for solvation: The case of protonated water clusters in helium
• ML Models of Vibrating H${}_2$CO: Comparing Reproducing Kernels, FCHL and PhysNet
• On the design space between molecular mechanics and machine learning force fields
• Lightweight and Effective Tensor Sensitivity for Atomistic Neural Networks
• MGNN: Moment Graph Neural Network for Universal Molecular Potentials
• Solvent quality and solvent polarity in polypeptides
• Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields
• Automated Microsolvation for Minimum Energy Path Construction in Solution
• Exciting DeePMD: Learning excited state energies, forces, and non-adiabatic couplings
• Iterative charge equilibration for fourth-generation high-dimensional neural network potentials
• Guest Editorial: Special Topic on Software for Atomistic Machine Learning
• Considerations in the use of ML interaction potentials for free energy calculations