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...

Full description

Saved in:

Bibliographic Details
Main Authors:	Amin Alibakhshi, Bernd Hartke
Format:	article
Language:	EN
Published:	Nature Portfolio 2021
Subjects:	Science Q
Online Access:	https://doaj.org/article/b437c0c0f0cc4539b1ced73dce0c8ccf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	oai:doaj.org-article:b437c0c0f0cc4539b1ced73dce0c8ccf
record_format	dspace
spelling	oai:doaj.org-article:b437c0c0f0cc4539b1ced73dce0c8ccf2021-12-02T17:40:27ZImproved prediction of solvation free energies by machine-learning polarizable continuum solvation model10.1038/s41467-021-23724-62041-1723https://doaj.org/article/b437c0c0f0cc4539b1ced73dce0c8ccf2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-23724-6https://doaj.org/toc/2041-1723Accurate 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 computational costs.Amin AlibakhshiBernd HartkeNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Amin Alibakhshi Bernd Hartke Improved prediction of solvation free energies by machine-learning polarizable continuum solvation model
description	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 computational costs.
format	article
author	Amin Alibakhshi Bernd Hartke
author_facet	Amin Alibakhshi Bernd Hartke
author_sort	Amin Alibakhshi
title	Improved prediction of solvation free energies by machine-learning polarizable continuum solvation model
title_short	Improved prediction of solvation free energies by machine-learning polarizable continuum solvation model
title_full	Improved prediction of solvation free energies by machine-learning polarizable continuum solvation model
title_fullStr	Improved prediction of solvation free energies by machine-learning polarizable continuum solvation model
title_full_unstemmed	Improved prediction of solvation free energies by machine-learning polarizable continuum solvation model
title_sort	improved prediction of solvation free energies by machine-learning polarizable continuum solvation model
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b437c0c0f0cc4539b1ced73dce0c8ccf
work_keys_str_mv	AT aminalibakhshi improvedpredictionofsolvationfreeenergiesbymachinelearningpolarizablecontinuumsolvationmodel AT berndhartke improvedpredictionofsolvationfreeenergiesbymachinelearningpolarizablecontinuumsolvationmodel
_version_	1718379784517451776

Improved prediction of solvation free energies by machine-learning polarizable continuum solvation model

Similar Items