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...
Saved in:
Main Authors: | Amin Alibakhshi, Bernd Hartke |
---|---|
Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/b437c0c0f0cc4539b1ced73dce0c8ccf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Simulating the ghost: quantum dynamics of the solvated electron
by: Jingang Lan, et al.
Published: (2021) -
Correction: Corrigendum: The solvation of electrons by an atmospheric-pressure plasma
by: Paul Rumbach, et al.
Published: (2016) -
Atomistic characterization of the active-site solvation dynamics of a model photocatalyst
by: Tim B. van Driel, et al.
Published: (2016) -
Solvation-Guided Design of Fluorescent Probes for Discrimination of Amyloids
by: Kevin J. Cao, et al.
Published: (2018) -
Creating solvation environments in heterogeneous catalysts for efficient biomass conversion
by: Qi Sun, et al.
Published: (2018)