Is machine learning redefining the perovskite solar cells?

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dc.authorid0000-0001-9852-7246en_US
dc.contributor.authorParikh, Nishi
dc.contributor.authorKaramta, Meera R.
dc.contributor.authorYadav, Neha
dc.contributor.authorMahdi Tavakoli, Mohammad
dc.contributor.authorProchowicz, Daniel
dc.contributor.authorAkın, Seçkin
dc.date.accessioned2021-08-24T06:58:44Z
dc.date.available2021-08-24T06:58:44Z
dc.date.issued2022en_US
dc.departmentKMÜ, Mühendislik Fakültesi, Metalurji ve Malzeme Mühendisliği Bölümüen_US
dc.descriptionWOS:000701749300011en_US
dc.description.abstractDevelopment of novel materials with desirable properties remains at the forefront of modern scientific research. Machine learning (ML), a branch of artificial intelligence, has recently emerged as a powerful technology in optoelectronic devices for the prediction of various properties and rational design of materials. Metal halide perovskites (MHPs) have been at the centre of attraction owing to their outstanding photophysical properties and rapid development in solar cell application. Therefore, the application of ML in the field of MHPs is also getting much attention to optimize the fabrication process and reduce the cost of processing. Here, we comprehensively reviewed different applications of ML in the designing of both MHP absorber layers as well as complete perovskite solar cells (PSCs). At the end, the challenges of ML along with the possible future direction of research are discussed. We believe that this review becomes an indispensable roadmap for optimizing materials composition and predicting design strategies in the field of perovskite technology in the future.en_US
dc.identifier.citationParikh, N., Karamta, M., Yadav, N., Mahdi Tavakoli, M., Prochowicz, D., Akın, S., . . . Yadav, P. (2022). Is machine learning redefining the perovskite solar cells? Journal of Energy Chemistry, 66, 74-90. doi:10.1016/j.jechem.2021.07.020en_US
dc.identifier.doi10.1016/j.jechem.2021.07.020
dc.identifier.endpage90en_US
dc.identifier.issn2095-4956
dc.identifier.scopus2-s2.0-85112386494
dc.identifier.scopusqualityQ1
dc.identifier.startpage74en_US
dc.identifier.urihttps://doi.org/10.1016/j.jechem.2021.07.020
dc.identifier.urihttps://hdl.handle.net/11492/5183
dc.identifier.volume66en_US
dc.identifier.wosWOS:000701749300011
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.institutionauthorAkın, Seçkin
dc.language.isoen
dc.publisherElsevier B.V.en_US
dc.relation.journalJournal of Energy Chemistryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLead Free Perovskitesen_US
dc.subjectMachine Learningen_US
dc.subjectMetal Halide Perovskitesen_US
dc.subjectPerovskite Solar Cellen_US
dc.titleIs machine learning redefining the perovskite solar cells?en_US
dc.typeArticle

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