Perspective on integrating machine learning into computational chemistry and materials science
| dc.contributor.author | Westermayr, Julia | |
| dc.contributor.author | Gastegger, Michael | |
| dc.contributor.author | Schütt, Kristof T. | |
| dc.contributor.author | Maurer, Reinhard J. | |
| dc.date.accessioned | 2021-11-15T09:28:59Z | |
| dc.date.available | 2021-11-15T09:28:59Z | |
| dc.date.issued | 2021-06-21 | |
| dc.description.abstract | Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly established in the construction of high-dimensional interatomic potentials. Not a day goes by without another proof of principle being published on how ML methods can represent and predict quantum mechanical properties—be they observable, such as molecular polarizabilities, or not, such as atomic charges. As ML is becoming pervasive in electronic structure theory and molecular simulation, we provide an overview of how atomistic computational modeling is being transformed by the incorporation of ML approaches. From the perspective of the practitioner in the field, we assess how common workflows to predict structure, dynamics, and spectroscopy are affected by ML. Finally, we discuss how a tighter and lasting integration of ML methods with computational chemistry and materials science can be achieved and what it will mean for research practice, software development, and postgraduate training. | en |
| dc.description.sponsorship | BMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and Data | en |
| dc.identifier.eissn | 1089-7690 | |
| dc.identifier.issn | 0021-9606 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/13882 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-12655 | |
| dc.language.iso | en | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject.ddc | 530 Physik | de |
| dc.subject.other | molecular dynamics | en |
| dc.subject.other | electronic-structure theory | en |
| dc.subject.other | materials science | en |
| dc.subject.other | molecular properties | en |
| dc.subject.other | computational chemistry | en |
| dc.subject.other | machine learning | en |
| dc.subject.other | molecular simulations | en |
| dc.subject.other | atomistic simulations | en |
| dc.subject.other | spectroscopy | en |
| dc.subject.other | quantum chemical dynamics | en |
| dc.title | Perspective on integrating machine learning into computational chemistry and materials science | en |
| dc.type | Article | en |
| dc.type.version | publishedVersion | en |
| dcterms.bibliographicCitation.articlenumber | 230903 | en |
| dcterms.bibliographicCitation.doi | 10.1063/5.0047760 | en |
| dcterms.bibliographicCitation.issue | 23 | en |
| dcterms.bibliographicCitation.journaltitle | The Journal of Chemical Physics | en |
| dcterms.bibliographicCitation.originalpublishername | American Institute of Physics | en |
| dcterms.bibliographicCitation.originalpublisherplace | Melville, NY | en |
| dcterms.bibliographicCitation.volume | 154 | en |
| tub.accessrights.dnb | domain | * |
| tub.affiliation | Fak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Maschinelles Lernen | de |
| tub.affiliation.faculty | Fak. 4 Elektrotechnik und Informatik | de |
| tub.affiliation.group | FG Maschinelles Lernen | de |
| tub.affiliation.institute | Inst. Softwaretechnik und Theoretische Informatik | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |