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http://dx.doi.org/10.18419/opus-13479
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DC Element | Wert | Sprache |
---|---|---|
dc.contributor.author | Gubaev, Konstantin | - |
dc.contributor.author | Zaverkin, Viktor | - |
dc.contributor.author | Srinivasan, Prashanth | - |
dc.contributor.author | Duff, Andrew Ian | - |
dc.contributor.author | Kästner, Johannes | - |
dc.contributor.author | Grabowski, Blazej | - |
dc.date.accessioned | 2023-09-13T07:51:09Z | - |
dc.date.available | 2023-09-13T07:51:09Z | - |
dc.date.issued | 2023 | de |
dc.identifier.issn | 2057-3960 | - |
dc.identifier.other | 1860362834 | - |
dc.identifier.uri | http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-134984 | de |
dc.identifier.uri | http://elib.uni-stuttgart.de/handle/11682/13498 | - |
dc.identifier.uri | http://dx.doi.org/10.18419/opus-13479 | - |
dc.description.abstract | Chemically complex multicomponent alloys possess exceptional properties derived from an inexhaustible compositional space. The complexity however makes interatomic potential development challenging. We explore two complementary machine-learned potentials - the moment tensor potential (MTP) and the Gaussian moment neural network (GM-NN) - in simultaneously describing configurational and vibrational degrees of freedom in the Ta-V-Cr-W alloy family. Both models are equally accurate with excellent performance evaluated against density-functional-theory. They achieve root-mean-square-errors (RMSEs) in energies of less than a few meV/atom across 0 K ordered and high-temperature disordered configurations included in the training. Even for compositions not in training, relative energy RMSEs at high temperatures are within a few meV/atom. High-temperature molecular dynamics forces have similarly small RMSEs of about 0.15 eV/Å for the disordered quaternary included in, and ternaries not part of training. MTPs achieve faster convergence with training size; GM-NNs are faster in execution. Active learning is partially beneficial and should be complemented with conventional human-based training set generation. | en |
dc.language.iso | en | de |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/865855 | de |
dc.relation.uri | doi:10.1038/s41524-023-01073-w | de |
dc.rights | info:eu-repo/semantics/openAccess | de |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | de |
dc.subject.ddc | 530 | de |
dc.title | Performance of two complementary machine-learned potentials in modelling chemically complex systems | en |
dc.type | article | de |
ubs.fakultaet | Chemie | de |
ubs.fakultaet | Fakultätsübergreifend / Sonstige Einrichtung | de |
ubs.institut | Institut für Materialwissenschaft | de |
ubs.institut | Institut für Theoretische Chemie | de |
ubs.institut | Fakultätsübergreifend / Sonstige Einrichtung | de |
ubs.publikation.seiten | 15 | de |
ubs.publikation.source | npj computational materials 9 (2023), No. 129 | de |
ubs.publikation.typ | Zeitschriftenartikel | de |
Enthalten in den Sammlungen: | 03 Fakultät Chemie |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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s41524-023-01073-w.pdf | 3,75 MB | Adobe PDF | Öffnen/Anzeigen |
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