A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces, and nanoparticles

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorKloppenburg, Jan
dc.contributor.authorPártay, Livia B.
dc.contributor.authorJónsson, Hannes
dc.contributor.authorCaro, Miguel A.
dc.contributor.departmentDAS Group
dc.contributor.departmentUniversity of Warwick
dc.contributor.departmentDepartment of Applied Physics
dc.contributor.departmentDepartment of Chemistry and Materials Scienceen
dc.contributor.departmentDepartment of Electrical Engineering and Automationen
dc.contributor.departmentDepartment of Applied Physicsen
dc.date.accessioned2023-04-26T08:39:42Z
dc.date.available2023-04-26T08:39:42Z
dc.date.issued2023-04-07
dc.descriptionJ.K. and M.A.C. acknowledge funding from the Academy of Finland under the C1 Value Programme, Project No. 329483. M.A.C. also acknowledges personal funding from the Academy of Finland, Project No. 330488. H.J. acknowledges funding from the Icelandic Research Fund, Project No. 207283-053. L.B.P. acknowledges support from the EPSRC through an Early Career Fellowship (Grant No. EP/T000163/1). Computational resources for this project were obtained from the CSC—IT Center for Science and Aalto University’s Science-IT project.
dc.description.abstractA Gaussian approximation machine learning interatomic potential for platinum is presented. It has been trained on density-functional theory (DFT) data computed for bulk, surfaces, and nanostructured platinum, in particular nanoparticles. Across the range of tested properties, which include bulk elasticity, surface energetics, and nanoparticle stability, this potential shows excellent transferability and agreement with DFT, providing state-of-the-art accuracy at a low computational cost. We showcase the possibilities for modeling of Pt systems enabled by this potential with two examples: the pressure-temperature phase diagram of Pt calculated using nested sampling and a study of the spontaneous crystallization of a large Pt nanoparticle based on classical dynamics simulations over several nanoseconds.en
dc.description.versionPeer revieweden
dc.format.extent9
dc.format.mimetypeapplication/pdf
dc.identifier.citationKloppenburg , J , Pártay , L B , Jónsson , H & Caro , M A 2023 , ' A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces, and nanoparticles ' , The Journal of chemical physics , vol. 158 , no. 13 , 134704 . https://doi.org/10.1063/5.0143891en
dc.identifier.doi10.1063/5.0143891
dc.identifier.issn0021-9606
dc.identifier.issn1089-7690
dc.identifier.otherPURE UUID: 59160df2-d3ac-4208-b011-9452780f7642
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/59160df2-d3ac-4208-b011-9452780f7642
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85151980977&partnerID=8YFLogxK
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/106805108/CHEM_Kloppenburg_et_al_A_general_purpose_2023_J_Chem_Phys.pdf
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/120528
dc.identifier.urnURN:NBN:fi:aalto-202304262850
dc.language.isoenen
dc.publisherAmerican Institute of Physics
dc.relation.ispartofseriesThe Journal of chemical physicsen
dc.relation.ispartofseriesVolume 158, issue 13en
dc.rightsopenAccessen
dc.titleA general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces, and nanoparticlesen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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