Machine learning-driven analysis of activation energy for metal halide perovskites

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dc.contributor.authorPatel, Vimi
dc.contributor.authorSorathia, Kunjrani
dc.contributor.authorUnjiya, Kushal
dc.contributor.authorPatel, Raj Dashrath
dc.contributor.authorPandey, Siddhi Vinayak
dc.contributor.authorAkın, Seçkin
dc.date.accessioned2025-03-13T05:27:45Z
dc.date.available2025-03-13T05:27:45Z
dc.date.issued2025
dc.departmentKMÜ, Mühendislik Fakültesi, Metalurji ve Malzeme Mühendisliği Bölümü
dc.description.abstractMetal halide perovskite single crystals (MHPSCs) are highly promising materials for optoelectronic applications, but their stability is hindered by ion migration, thereby impacting their performance. A key factor to understand this issue is calculating the activation energy. Electrochemical Impedance Spectroscopy (EIS) is a powerful technique for separating ionic and electronic processes, yet traditional analysis is labour-intensive, involving extensive measurements, circuit fitting, and manual data interpretation. In this study, we introduce a machine learning (ML)-driven approach to fully automate EIS analysis. EIS data, collected for MAPbI3 and MAPbBr3 across temperatures from 263 K to 343 K, enabled the creation of a large database. The developed ML model predicts EIS spectra at unknown temperatures, fits the appropriate electrical circuit, and automatically extracts passive component values to calculate the activation energy via an Arrhenius plot. This automated workflow streamlines the calculation process, offering fast and reliable activation energy predictions even when temperature data are incomplete or missing. Our approach enhances the efficiency of EIS analysis, providing valuable insights into the stability and performance of MHP SCs.
dc.identifier.citationPatel, V., Sorathia, K., Unjiya, K., Patel, R. D., Pandey, S. V., Kalam, A., Prochowicz, D., Akin, S., & Yadav, P. (2025). Machine learning-driven analysis of activation energy for metal halide perovskites. Dalton Transactions (Cambridge, England : 2003), 54(11), 4637–4644. https://doi.org/10.1039/d4dt03123g
dc.identifier.doi10.1039/d4dt03123g
dc.identifier.issn1477-9226
dc.identifier.issn1477-9234
dc.identifier.pmid39964116
dc.identifier.urihttps://doi.org/10.1039/d4dt03123g
dc.identifier.urihttps://hdl.handle.net/11492/10627
dc.identifier.wosWOS:001423676700001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.indekslendigikaynakScopus
dc.institutionauthorAkın, Seçkin
dc.institutionauthoridAkın, Seçkin/0000-0001-9852-7246
dc.language.isoen
dc.publisherRoyal Soc Chemistry
dc.relation.ispartofDalton Transactions
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectMigration
dc.subjectTransport
dc.subjectSolar-Cells
dc.titleMachine learning-driven analysis of activation energy for metal halide perovskites
dc.typeArticle

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