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dc.contributor.authorKeith, John A.-
dc.contributor.authorVassilev-Galindo, Valentin-
dc.contributor.authorCheng, Bingqing-
dc.contributor.authorChmiela, Stefan-
dc.contributor.authorGastegger, Michael-
dc.contributor.authorMüller, Klaus-Robert-
dc.contributor.authorTkatchenko, Alexandre-
dc.date.accessioned2021-11-04T10:29:27Z-
dc.date.available2021-11-04T10:29:27Z-
dc.date.issued2021-07-07-
dc.identifier.issn0009-2665-
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/13808-
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-12584-
dc.description.abstractMachine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.en
dc.description.sponsorshipBMBF, 01IS14013A, Verbundprojekt: BBDC - Berliner Kompetenzzentrum für Big Dataen
dc.description.sponsorshipBMBF, 01GQ1115, D-JPN Verbund: Adaptive Gehirn-Computer-Schnittstellen (BCI) in nichtstationären Umgebungenen
dc.description.sponsorshipBMBF, 01GQ0850, Verbundprojekt: Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktion - Teilprojekte A1, A3, A4, B4, W3, Zentrumen
dc.description.sponsorshipBMBF, 01IS18025A, Verbundprojekt BIFOLD-BBDC: Berlin Institute for the Foundations of Learning and Dataen
dc.description.sponsorshipBMBF, 031L0207D, CompLS - Runde 2 - Verbundprojekt: Patho234 - Multidimensionale Bildanalyse von reaktiven und neoplastischen Lymphknoten durch maschinelles Lernen - Teilprojekt Den
dc.description.sponsorshipBMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and Dataen
dc.description.sponsorshipDFG, 390685689, EXC 2046: MATH+: Berlin Mathematics Research Centeren
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subject.ddc540 Chemie und zugeordnete Wissenschaftende
dc.subject.otherelectrical energyen
dc.subject.otherquantum mechanicsen
dc.subject.otherpotential energyen
dc.subject.othermaterialsen
dc.subject.othermoleculesen
dc.titleCombining machine learning and computational chemistry for predictive insights into chemical systemsen
dc.typeArticleen
tub.accessrights.dnbfreeen
tub.publisher.universityorinstitutionTechnische Universität Berlinen
dc.identifier.eissn1520-6890-
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.doi10.1021/acs.chemrev.1c00107en
dcterms.bibliographicCitation.journaltitleChemical Reviewsen
dcterms.bibliographicCitation.originalpublisherplaceWashington, DCen
dcterms.bibliographicCitation.volume121en
dcterms.bibliographicCitation.pageend9872en
dcterms.bibliographicCitation.pagestart9816en
dcterms.bibliographicCitation.originalpublishernameAmerican Chemical Society (ACS)en
dcterms.bibliographicCitation.issue16en
tub.affiliationFak. 4 Elektrotechnik und Informatik » Inst. Softwaretechnik und Theoretische Informatik » FG Maschinelles Lernende
Appears in Collections:Technische Universität Berlin » Publications

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