Solvation Enthalpies and Free Energies for Organic Solvents through a Dense Neural Network: A Generalized-Born Approach

Abstract

A dense artificial neural network, ESE-ΔH-DNN, with two hidden layers for calculating both solvation free energies ΔG°solv and enthalpies ΔH°solv for neutral solutes in organic solvents is proposed. The input features are generalized-Born-type monatomic and pair electrostatic terms, the molecular volume, and atomic surface areas of the solute, as well as five easily available properties of the solvent. ESE-ΔH-DNN is quite accurate for ΔG°solv, with an RMSE (root mean square error) below 0.6 kcal/mol and an MAE (mean absolute error) well below 0.4 kcal/mol. It performs particularly well for alkane, aromatic, ester, and ketone solvents. ESE-ΔH-DNN also exhibits a fairly good accuracy for ΔH°solv prediction, with an RMSE below 1 kcal/mol and an MAE of about 0.6 kcal/mol