dc.date.accessioned | 2020-05-24T18:13:38Z | |
dc.date.available | 2021-04-03T22:45:42Z | |
dc.date.created | 2019-06-18T09:08:24Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Bondevik, Tarjei Kuwabara, Akihide Løvvik, Ole Martin . Application of machine learning-based selective sampling to determine BaZrO3 grain boundary structures. Computational materials science. 2019, 164, 57-65 | |
dc.identifier.uri | http://hdl.handle.net/10852/76197 | |
dc.description.abstract | A selective sampling procedure is applied to reduce the number of density functional theory calculations needed to find energetically favorable grain boundary structures. The procedure is based on a machine learning algorithm involving a Gaussian process, and uses statistical modelling to map the energies of the all grain boundaries. Using the procedure, energetically favorable grain boundaries in BaZrO3 are identified with up to 85% lower computational cost than the brute force alternative of calculating all possible structures. Furthermore, our results suggest that using a grid size of 0.3 Å in each dimension is sufficient when creating grain boundary structures using such sampling procedures. | |
dc.language | EN | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.title | Application of machine learning-based selective sampling to determine BaZrO3 grain boundary structures | |
dc.type | Journal article | |
dc.creator.author | Bondevik, Tarjei | |
dc.creator.author | Kuwabara, Akihide | |
dc.creator.author | Løvvik, Ole Martin | |
cristin.unitcode | 185,15,17,0 | |
cristin.unitname | Senter for materialvitenskap og nanoteknologi | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 2 | |
dc.identifier.cristin | 1705504 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Computational materials science&rft.volume=164&rft.spage=57&rft.date=2019 | |
dc.identifier.jtitle | Computational materials science | |
dc.identifier.volume | 164 | |
dc.identifier.startpage | 57 | |
dc.identifier.endpage | 65 | |
dc.identifier.doi | https://doi.org/10.1016/j.commatsci.2019.03.054 | |
dc.identifier.urn | URN:NBN:no-79317 | |
dc.type.document | Tidsskriftartikkel | |
dc.type.peerreviewed | Peer reviewed | |
dc.source.issn | 0927-0256 | |
dc.identifier.fulltext | Fulltext https://www.duo.uio.no/bitstream/handle/10852/76197/1/Applying%2BMachine%2BLearning-based%2BSelective%2BSampling%2Bto%2Bfind%2BGrain%2BBoundary%2BStructures.pdf | |
dc.type.version | AcceptedVersion | |
dc.relation.project | NFR/228355 | |