Extending machine learning model for implicit solvation to free energy calculations

ArXi:2510.20103v2 Announce Type: replace-cross The implicit solvent approach offers a computationally efficient framework to model solvation effects in molecular simulations. However, its accuracy often falls short compared to explicit solvent models, limiting its use in precise thermodynamic calculations. Recent advancements in machine learning (ML) present an opportunity to overcome these limitations by leveraging neural networks to develop precise implicit solvent potentials for diverse applications.