File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Improving the Performance of Long-Range-Corrected Exchange-Correlation Functional with an Embedded Neural Network

TitleImproving the Performance of Long-Range-Corrected Exchange-Correlation Functional with an Embedded Neural Network
Authors
Issue Date2017
PublisherAmerican Chemical Society. The Journal's web site is located at http://pubs.acs.org/jpca
Citation
The Journal of Physical Chemistry A, 2017, v. 121 n. 39, p. 7273-7281 How to Cite?
AbstractA machine-learning-based exchange-correlation functional is proposed for general-purpose density functional theory calculations. It is built upon the long-range-corrected Becke-Lee-Yang-Parr (LC-BLYP) functional, along with an embedded neural network which determines the value of the range-separation parameter μ for every individual system. The structure and the weights of the neural network are optimized with a reference data set containing 368 highly accurate thermochemical and kinetic energies. The newly developed functional (LC-BLYP-NN) achieves a balanced performance for a variety of energetic properties investigated. It largely improves the accuracy of atomization energies and heats of formation on which the original LC-BLYP with a fixed μ performs rather poorly. Meanwhile, it yields a similar or slightly compromised accuracy for ionization potentials, electron affinities, and reaction barriers, for which the original LC-BLYP works reasonably well. This work clearly highlights the potential usefulness of machine-learning techniques for improving density functional calculations. (Figure Presented). © 2017 American Chemical Society.
Persistent Identifierhttp://hdl.handle.net/10722/279304
ISSN
2019 Impact Factor: 2.6
2015 SCImago Journal Rankings: 1.231
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLiu, Q-
dc.contributor.authorWang, JC-
dc.contributor.authorDu, PL-
dc.contributor.authorHu, LH-
dc.contributor.authorZheng, X-
dc.contributor.authorChen, G-
dc.date.accessioned2019-10-25T13:53:08Z-
dc.date.available2019-10-25T13:53:08Z-
dc.date.issued2017-
dc.identifier.citationThe Journal of Physical Chemistry A, 2017, v. 121 n. 39, p. 7273-7281-
dc.identifier.issn1089-5639-
dc.identifier.urihttp://hdl.handle.net/10722/279304-
dc.description.abstractA machine-learning-based exchange-correlation functional is proposed for general-purpose density functional theory calculations. It is built upon the long-range-corrected Becke-Lee-Yang-Parr (LC-BLYP) functional, along with an embedded neural network which determines the value of the range-separation parameter μ for every individual system. The structure and the weights of the neural network are optimized with a reference data set containing 368 highly accurate thermochemical and kinetic energies. The newly developed functional (LC-BLYP-NN) achieves a balanced performance for a variety of energetic properties investigated. It largely improves the accuracy of atomization energies and heats of formation on which the original LC-BLYP with a fixed μ performs rather poorly. Meanwhile, it yields a similar or slightly compromised accuracy for ionization potentials, electron affinities, and reaction barriers, for which the original LC-BLYP works reasonably well. This work clearly highlights the potential usefulness of machine-learning techniques for improving density functional calculations. (Figure Presented). © 2017 American Chemical Society.-
dc.languageeng-
dc.publisherAmerican Chemical Society. The Journal's web site is located at http://pubs.acs.org/jpca-
dc.relation.ispartofThe Journal of Physical Chemistry A-
dc.rightsThis document is the Accepted Manuscript version of a Published Work that appeared in final form in [JournalTitle], copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see [insert ACS Articles on Request author-directed link to Published Work, see http://pubs.acs.org/page/policy/articlesonrequest/index.html].-
dc.titleImproving the Performance of Long-Range-Corrected Exchange-Correlation Functional with an Embedded Neural Network-
dc.typeArticle-
dc.identifier.emailChen, G: ghchen@hku.hk-
dc.identifier.authorityChen, G=rp00671-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1021/acs.jpca.7b07045-
dc.identifier.pmid28876064-
dc.identifier.scopuseid_2-s2.0-85030532911-
dc.identifier.hkuros308245-
dc.identifier.volume121-
dc.identifier.issue39-
dc.identifier.spage7273-
dc.identifier.epage7281-
dc.identifier.isiWOS:000412149600023-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats