Improved prediction of solvation free energies by machine-learning polarizable continuum solvation model
Accurate theoretical evaluation of solvation free energy is challenging. Here the authors introduce a machine-learning based polarizable continuum solvation approach to improve the accuracy of widely accepted continuum solvation models by almost one order of magnitude without additional computationa...
Enregistré dans:
Auteurs principaux: | Amin Alibakhshi, Bernd Hartke |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/b437c0c0f0cc4539b1ced73dce0c8ccf |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Simulating the ghost: quantum dynamics of the solvated electron
par: Jingang Lan, et autres
Publié: (2021) -
Correction: Corrigendum: The solvation of electrons by an atmospheric-pressure plasma
par: Paul Rumbach, et autres
Publié: (2016) -
Atomistic characterization of the active-site solvation dynamics of a model photocatalyst
par: Tim B. van Driel, et autres
Publié: (2016) -
Solvation-Guided Design of Fluorescent Probes for Discrimination of Amyloids
par: Kevin J. Cao, et autres
Publié: (2018) -
Creating solvation environments in heterogeneous catalysts for efficient biomass conversion
par: Qi Sun, et autres
Publié: (2018)