Atomic Neural Network for Calculation of Solvation Free Energies in Organic Solvents

ABSTRACTThis paper introduces AtomicESE, an artificial neural network for calculating solvation‐free energies ΔG°solv of molecules in organic solvents. AtomicESE calculates ΔG°solv by summing atomic contributions, each evaluated by a dense neural network. This atomic network uses 13 physically relevant input features, comprising six local atomic features, two global charge‐related molecular properties, and five solvent‐specific properties. For neutral solutes, AtomicESE achieves an average RMSE below 0.6 kcal/mol, demonstrating strong performance across all solvent classes, with particularly high accuracy for aromatic, haloaromatic, alkane, and ketone solvents. AtomicESE also works reliably for ionic solutes.