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AuthorGroß, Axeldc.contributor.author
Date of accession2022-11-24T10:55:02Zdc.date.accessioned
Available in OPARU since2022-11-24T10:55:02Zdc.date.available
Date of first publication2022-11-24dc.date.issued
AbstractDue to the ever-increasing improvement in computer power and the development of more efficient codes, atomistic first-principles calculations have become an indispensable tool in materials and interface chemistry. They are no longer limited to explanatory purposes but have gained predictive power, so that computational modeling and experiment can collaborate on the same footing. Still, quantum chemical first-principles methods are computationally expensive, and there are certain limitations as far as their scaling behavior in high-performance computing is concerned. Hence computational methods based on machine learning have become increasingly popular as an alternative approach to study materials and interfaces. Here some success stories of both approaches will be presented and their respective advantages and disadvantages will be critically discussed.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
Is part ofhttp://dx.doi.org/10.18725/OPARU-46164dc.relation.ispartof
LicenseCC BY 4.0 Internationaldc.rights
Link to license texthttps://creativecommons.org/licenses/by/4.0/dc.rights.uri
Keywordmaterials and interface chemistrydc.subject
Keyworddescriptorsdc.subject
Keywordelectrochemical energy conversion and storagedc.subject
Dewey Decimal GroupDDC 620 / Engineering & allied operationsdc.subject.ddc
LCSHQuantum chemistrydc.subject.lcsh
LCSHMachine learningdc.subject.lcsh
LCSHSolid-liquid interfacesdc.subject.lcsh
TitleQuantum chemistry and machine learning in computational materials and interface chemistrydc.title
Resource typeBeitrag zu einer Konferenzdc.type
VersionpublishedVersiondc.description.version
DOIhttp://dx.doi.org/10.18725/OPARU-46060dc.identifier.doi
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-46136-0dc.identifier.urn
GNDQuantenchemiedc.subject.gnd
GNDMaschinelles Lernendc.subject.gnd
InstitutionKommunikations- und Informationszentrum (kiz)uulm.affiliationSpecific
Peer reviewjauulm.peerReview
DCMI TypeTextuulm.typeDCMI
CategoryPublikationenuulm.category
Source - Title of sourceProceedings of the 7th bwHPC Symposiumsource.title
Quellenangabe - HerausgeberUniversität Ulmsource.contributor.editor1
Source - PublisherUlmsource.publisherPlace
Source - Volume7source.volume
Source - Year2022source.year
Source - From page17source.fromPage
Source - To page22source.toPage
Source - Article number4source.articleNumber
Source - ISBN978-3-948303-29-7source.identifier.isbn
Conference name7th bwHPC Symposiumuulm.conferenceName
Conference placeUlm University (online event)uulm.conferencePlace
Conference start date2021-11-08uulm.conferenceStartDate
Conference end date2021-11-08uulm.conferenceEndDate
Bibliographyuulmuulm.bibliographie
DFG project uulmHPC Forschungscluster (bwForCluster) Computergestützte Chemie und Quantenwissenschaften / DFG / 405998092 [INST 40/575-1 FUGG]uulm.projectDFG
DFG project uulmJUSTUS 2 / HPC Forschungscluster (bwForCluster) Computergestützte Chemie und Quantenwissenschaften / DFG / 405998092 [INST 40/467-1 FUGG]uulm.projectDFG
DFG project uulmEXC 2154 / POLiS / POLiS - Post Lithium Storage Cluster of Excellence / DFG / 390874152uulm.projectDFG


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