2026-04-13T01:08:24Zhttps://recercat.cat/oai/requestoai:recercat.cat:10256/254222024-10-29T23:15:42Zcom_2072_452955com_2072_2054col_2072_453073
RECERCAT
author
Vyboishchikov, Sergei F.
2024-10-29T23:15:41Z
2024-10-29T23:15:41Z
2024-08-12
http://hdl.handle.net/10256/25422
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
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess
Solvatació
Solvation
Solució (Química)
Solution (Chemistry)
Dissolvents
Solvents
Solvation Enthalpies and Free Energies for Organic Solvents through a Dense Neural Network: A Generalized-Born Approach
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion peer-reviewed