Roadmap on Machine learning in electronic structure

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dc.contributor.authorKulik, Heather J.
dc.contributor.authorHammerschmidt, Thomas
dc.contributor.authorSchmidt, J.
dc.contributor.authorBotti, Silvana
dc.contributor.authorMarques, Miguel A. L.
dc.contributor.authorBoley, M.
dc.contributor.authorScheffler, M.
dc.contributor.authorTodorović, Milica
dc.contributor.authorRinke, Patrick
dc.contributor.authorOses, Corey
dc.contributor.authorSmolyanyuk, Andriy
dc.contributor.authorCurtarolo, Stefano
dc.contributor.authorTkatchenko, A.
dc.contributor.authorBartók, Albert P.
dc.contributor.authorManzhos, Sergei
dc.contributor.authorIhara, M.
dc.contributor.authorCarrington, Tucker
dc.contributor.authorBehler, Jörg
dc.contributor.authorIsayev, Olexandr
dc.contributor.authorVeit, Max
dc.contributor.authorGrisafi, Andrea
dc.contributor.authorNigam, Jigyasa
dc.contributor.authorCeriotti, Michele
dc.contributor.authorSchütt, Kristof T.
dc.contributor.authorWestermayr, Julia
dc.contributor.authorGastegger, Michael
dc.contributor.authorMaurer, Reinhard J.
dc.contributor.authorKalita, B.
dc.contributor.authorBurke, Kieron
dc.contributor.authorNagai, R.
dc.contributor.authorAkashi, R.
dc.contributor.authorSugino, O.
dc.contributor.authorHermann, J.
dc.contributor.authorNoé, Frank
dc.contributor.authorPilati, Sebastiano
dc.contributor.authorDraxl, Claudia
dc.contributor.authorKuban, M.
dc.contributor.authorRigamonti, S.
dc.contributor.authorScheidgen, M.
dc.contributor.authorEsters, Marco
dc.contributor.authorHicks, David
dc.contributor.authorToher, Cormac
dc.contributor.authorBalachandran, Prasanna Venkataraman
dc.contributor.authorTamblyn, Isaac
dc.contributor.authorWhitelam, S.
dc.contributor.authorBellinger, C.
dc.contributor.authorGhiringhelli, Luca M.
dc.date.accessioned2022-09-07T09:58:18Z
dc.date.available2022-09-07T09:58:18Z
dc.date.issued2022-08-19
dc.date.updated2022-08-25T02:52:33Z
dc.description.abstractIn recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century.en
dc.description.sponsorshipEC/H2020/676580/EU/The Novel Materials Discovery Laboratory/NoMaDen
dc.identifier.eissn2516-1075
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/17377
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-16158
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc004 Datenverarbeitung; Informatikde
dc.subject.othermachine learningen
dc.subject.otherelectronic structureen
dc.subject.othercomputational materials scienceen
dc.subject.otherdensity-functional theoryen
dc.titleRoadmap on Machine learning in electronic structureen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumber023004en
dcterms.bibliographicCitation.doi10.1088/2516-1075/ac572fen
dcterms.bibliographicCitation.issue2en
dcterms.bibliographicCitation.journaltitleElectronic Structureen
dcterms.bibliographicCitation.originalpublishernameIOPen
dcterms.bibliographicCitation.originalpublisherplaceBristolen
dcterms.bibliographicCitation.volume4en
tub.accessrights.dnbfreeen
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Maschinelles Lernende
tub.affiliation.facultyFak. 4 Elektrotechnik und Informatikde
tub.affiliation.groupFG Maschinelles Lernende
tub.affiliation.instituteInst. Softwaretechnik und Theoretische Informatikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen

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