Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints
dc.contributor.author | Ihalage, A | |
dc.contributor.author | Hao, Y | |
dc.date.accessioned | 2021-06-16T10:01:06Z | |
dc.date.available | 2021-06-16T10:01:06Z | |
dc.date.issued | 2021-05-21 | |
dc.identifier.citation | Ihalage, Achintha, and Yang Hao. "Analogical Discovery Of Disordered Perovskite Oxides By Crystal Structure Information Hidden In Unsupervised Material Fingerprints". Npj Computational Materials, vol 7, no. 1, 2021. Springer Science And Business Media LLC, doi:10.1038/s41524-021-00536-2. Accessed 16 June 2021. | en_US |
dc.identifier.issn | 2057-3960 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/72562 | |
dc.description.abstract | Compositional disorder induces myriad captivating phenomena in perovskites. Target-driven discovery of perovskite solid solutions has been a great challenge due to the analytical complexity introduced by disorder. Here, we demonstrate that an unsupervised deep learning strategy can find fingerprints of disordered materials that embed perovskite formability and underlying crystal structure information by learning only from the chemical composition, manifested in (A1−xA′x)BO3 and A(B1−xB′x)O3 formulae. This phenomenon can be capitalized to predict the crystal symmetry of experimental compositions, outperforming several supervised machine learning (ML) algorithms. The educated nature of material fingerprints has led to the conception of analogical materials discovery that facilitates inverse exploration of promising perovskites based on similarity investigation with known materials. The search space of unstudied perovskites is screened from ~600,000 feasible compounds using experimental data powered ML models and automated web mining tools at a 94% success rate. This concept further provides insights on possible phase transitions and computational modelling of complex compositions. The proposed quantitative analysis of materials analogies is expected to bridge the gap between the existing materials literature and the undiscovered terrain. | en_US |
dc.format.extent | 75 - ? | |
dc.publisher | Nature Publishing Group | en_US |
dc.relation.ispartof | npj Computational Materials | |
dc.rights | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. | |
dc.rights | Attribution 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/us/ | * |
dc.title | Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2021, The Author(s) | |
dc.identifier.doi | 10.1038/s41524-021-00536-2 | |
pubs.issue | 1 | en_US |
pubs.notes | Not known | en_US |
pubs.volume | 7 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | SOFTWARE DEFINED MATERIALS FOR DYNAMIC CONTROL OF ELECTROMAGNETIC WAVES (ANIMATE)::Engineering and Physical Sciences Research Council | en_US |
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