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dc.contributor.authorNguyen, Thuong Ten
dc.contributor.authorSzékely, Eszteren
dc.contributor.authorImbalzano, Giulioen
dc.contributor.authorBehler, Jörgen
dc.contributor.authorCsányi, Gáboren
dc.contributor.authorCeriotti, Micheleen
dc.contributor.authorGötz, Andreas Wen
dc.contributor.authorPaesani, Francescoen
dc.date.accessioned2018-08-23T13:35:49Z
dc.date.available2018-08-23T13:35:49Z
dc.date.issued2018-06-28en
dc.identifier.issn0021-9606
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/278996
dc.description.abstractThe accurate representation of multidimensional potential energy surfaces is a necessary requirement for realistic computer simulations of molecular systems. The continued increase in computer power accompanied by advances in correlated electronic structure methods nowadays enables routine calculations of accurate interaction energies for small systems, which can then be used as references for the development of analytical potential energy functions (PEFs) rigorously derived from many-body (MB) expansions. Building on the accuracy of the MB-pol many-body PEF, we investigate here the performance of permutationally invariant polynomials (PIPs), neural networks, and Gaussian approximation potentials (GAPs) in representing water two-body and three-body interaction energies, denoting the resulting potentials PIP-MB-pol, Behler-Parrinello neural network-MB-pol, and GAP-MB-pol, respectively. Our analysis shows that all three analytical representations exhibit similar levels of accuracy in reproducing both two-body and three-body reference data as well as interaction energies of small water clusters obtained from calculations carried out at the coupled cluster level of theory, the current gold standard for chemical accuracy. These results demonstrate the synergy between interatomic potentials formulated in terms of a many-body expansion, such as MB-pol, that are physically sound and transferable, and machine-learning techniques that provide a flexible framework to approximate the short-range interaction energy terms.
dc.description.sponsorshipThis work was supported by the National Science Foundation through Grant No. ACI-1642336 (to F.P. and A.W.G.). This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation Grant No. ACI-1548562. J.B. is grateful for a Heisenberg professorship funded by the DFG (No. Be3264/11-2). E.Sz. would like to acknowledge the support of the Peterhouse Research Studentship and the support of BP International Centre for Advanced Materials (ICAM). M.C. was supported by the European Research Council under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 677013-HBMAP). G.I. acknowledges funding from the Fondazione Zegna
dc.languageengen
dc.publisherAIP
dc.titleComparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions.en
dc.typeArticle
prism.endingPage241725
prism.issueIdentifier24en
prism.publicationDate2018en
prism.publicationNameJ Chem Physen
prism.startingPage241725
prism.volume148en
dc.identifier.doi10.17863/CAM.22160
dcterms.dateAccepted2018-03-01en
rioxxterms.versionofrecord10.1063/1.5024577en
rioxxterms.versionVoR*
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2018-06-28en
dc.identifier.eissn1089-7690
rioxxterms.typeJournal Article/Reviewen
cam.issuedOnline2018-04-09en
dc.identifier.urlhttps://aip.scitation.org/doi/10.1063/1.5024577en
cam.orpheus.successThu Jan 30 12:59:57 GMT 2020 - The item has an open VoR version.*
rioxxterms.freetoread.startdate2100-01-01


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