2026-04-13T09:52:42Zhttps://recercat.cat/oai/request

oai:recercat.cat:10256/246352024-10-29T23:15:09Zcom_2072_452955com_2072_2054col_2072_453073

00925njm 22002777a 4500 dc Vyboishchikov, Sergei F. author 2023-11-14 I propose a dense Neural Network, ESE-GB-DNN, for evaluation of solvation free energies ΔG°solv for molecules and ions in water and nonaqueous solvents. As input features, it employs generalized-Born monatomic and diatomic terms, as well as atomic surface areas and the molecular volume. The electrostatics calculation is based on a specially modified version of electronegativity-equalization atomic charges. ESE-GB-DNN evaluates ΔG°solv in a simple and highly efficient way, yet it offers a high accuracy, often challenging that of standard DFT-based methods. For neutral solutes, ESE-GB-DNN yields an RMSE between 0.7 and 1.3 kcal/mol, depending on the solvent class. ESE-GB-DNN performs particularly well for nonaqueous solutions of ions, with an RMSE of about 0.7 kcal/mol. For ions in water, the RMSE is larger (2.9 kcal/mol) Financial support from the Spanish Ministerio de Ciencia, Innovación y Universidades (grant PID2020-113711GB-I00) is gratefully appreciated Open Access funding provided thanks to the CRUE-CSIC agreement with American Chemical Society (ACS) Solvatació Solvation Solució (Química) Solution (Chemistry) Química -- Informàtica Chemistry -- Data processing Predicting Solvation Free Energies Using Electronegativity-Equalization Atomic Charges and a Dense Neural Network: A Generalized-Born Approach