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dc.contributor.authorBernstein, Noamen
dc.contributor.authorBhattarai, Bishalen
dc.contributor.authorCsanyi, Gaboren
dc.contributor.authorDrabold, David Aen
dc.contributor.authorElliott, Stephenen
dc.contributor.authorDeringer, Volkeren
dc.date.accessioned2019-04-24T11:03:04Z
dc.date.available2019-04-24T11:03:04Z
dc.date.issued2019-05-20en
dc.identifier.issn1433-7851
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/291938
dc.description.abstractAmorphous materials are being described by increasingly powerful computer simulations, but new approaches are still needed to fully understand their intricate atomic structures. Here, we show how machine-learning-based techniques can give new, quantitative chemical insight into the atomic-scale structure of amorphous silicon (a-Si). We com- bine a quantitative description of the nearest- and next-nearest- neighbor structure with a quantitative description of local stability. The analysis is applied to an ensemble of a-Si networks in which we tailor the degree of ordering by varying the quench rates down to 10^10 K/s. Our approach associates coordination defects in a-Si with distinct stability regions and it has also been applied to liquid Si, where it traces a clear-cut transition in local energies during vitrification. The method is straightforward and inexpensive to apply, and therefore expected to have more general significance for developing a quantitative understanding of liquid and amorphous states of
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectamorphous materialsen
dc.subjectcomputational chemistryen
dc.subjectcontinuous random networksen
dc.subjectmachine learningen
dc.subjectsiliconen
dc.titleQuantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Siliconen
dc.typeArticle
prism.endingPage7061
prism.issueIdentifier21en
prism.publicationDate2019en
prism.publicationNameANGEWANDTE CHEMIE-INTERNATIONAL EDITIONen
prism.startingPage7057
prism.volume58en
dc.identifier.doi10.17863/CAM.39093
dcterms.dateAccepted2019-03-05en
rioxxterms.versionofrecord10.1002/anie.201902625en
rioxxterms.versionVoR*
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2019-05-20en
dc.contributor.orcidCsanyi, Gabor [0000-0002-8180-2034]
dc.contributor.orcidDeringer, Volker [0000-0001-6873-0278]
dc.identifier.eissn1521-3773
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idIsaac Newton Trust (17.08(c))
pubs.funder-project-idLeverhulme Trust (ECF-2017-278)
pubs.funder-project-idEPSRC (EP/P022596/1)


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International