Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/165413
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dc.contributor.authorAnand, D. Vijayen_US
dc.contributor.authorXu, Qiangen_US
dc.contributor.authorWee, Junjieen_US
dc.contributor.authorXia, Kelinen_US
dc.contributor.authorSum, Tze Chienen_US
dc.date.accessioned2023-03-27T03:06:03Z-
dc.date.available2023-03-27T03:06:03Z-
dc.date.issued2022-
dc.identifier.citationAnand, D. V., Xu, Q., Wee, J., Xia, K. & Sum, T. C. (2022). Topological feature engineering for machine learning based halide perovskite materials design. Npj Computational Materials, 8(1). https://dx.doi.org/10.1038/s41524-022-00883-8en_US
dc.identifier.issn2057-3960en_US
dc.identifier.urihttps://hdl.handle.net/10356/165413-
dc.description.abstractAccelerated materials development with machine learning (ML) assisted screening and high throughput experimentation for new photovoltaic materials holds the key to addressing our grand energy challenges. Data-driven ML is envisaged as a decisive enabler for new perovskite materials discovery. However, its full potential can be severely curtailed by poorly represented molecular descriptors (or fingerprints). Optimal descriptors are essential for establishing effective mathematical representations of quantitative structure-property relationships. Here we reveal that our persistent functions (PFs) based learning models offer significant accuracy advantages over traditional descriptor based models in organic-inorganic halide perovskite (OIHP) materials design and have similar performance as deep learning models. Our multiscale simplicial complex approach not only provides a more precise representation for OIHP structures and underlying interactions, but also has better transferability to ML models. Our results demonstrate that advanced geometrical and topological invariants are highly efficient feature engineering approaches that can markedly improve the performance of learning models for molecular data analysis. Further, new structure-property relationships can be established between our invariants and bandgaps. We anticipate that our molecular representations and featurization models will transcend the limitations of conventional approaches and lead to breakthroughs in perovskite materials design and discovery.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationM4081842.110en_US
dc.relationRG109/19en_US
dc.relationMOE-T2EP50120-0004en_US
dc.relationMOE-T2EP20120-0013en_US
dc.relationNRF-NRFI-2018-04en_US
dc.relation.ispartofnpj Computational Materialsen_US
dc.relation.uri10.21979/N9/CVJWZ9en_US
dc.rights© The Author(s) 2022. 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/.en_US
dc.subjectScience::Physicsen_US
dc.titleTopological feature engineering for machine learning based halide perovskite materials designen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.identifier.doi10.1038/s41524-022-00883-8-
dc.description.versionPublished versionen_US
dc.identifier.scopus2-s2.0-85138709678-
dc.identifier.issue1en_US
dc.identifier.volume8en_US
dc.subject.keywordsFeature Engineeringen_US
dc.subject.keywordsHalide Perovskitesen_US
dc.description.acknowledgementThis work was supported in part by Nanyang Technological University Startup Grant M4081842.110, Singapore Ministry of Education Academic Research fund Tier 1 grant RG109/19 and Tier 2 grants MOE-T2EP50120-0004 and MOE-T2EP20120-0013, as well as the National Research Foundation (NRF), Singapore under its NRF Investigatorship (NRF-NRFI2018-04).en_US
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