Probing the Low-Frequency Response of Impedance Spectroscopy of Halide Perovskite Single Crystals Using Machine Learning

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dc.contributor.authorParikh, Nishi
dc.contributor.authorAkin, Seckin
dc.contributor.authorKalam, Abul
dc.contributor.authorProchowicz, Daniel
dc.contributor.authorYadav, Pankaj
dc.date.accessioned2024-02-23T14:16:36Z
dc.date.available2024-02-23T14:16:36Z
dc.date.issued2023
dc.departmentNEÜen_US
dc.description.abstractElectrochemical impedance spectroscopy (EIS) has emergedas a versatiletechnique for characterization and analysis of metal halide perovskitesolar cells (PSCs). The crucial information about ion migration andcarrier accumulation in PSCs can be extracted from the low-frequencyregime of the EIS spectrum. However, lengthy measurement time at lowfrequencies along with material degradation due to prolonged exposureto light and bias motivates the use of machine learning (ML) in predictingthe low-frequency response. Here, we have developed an ML model topredict the low-frequency response of the halide perovskite singlecrystals. We first synthesized high-quality MAPbBr(3) singlecrystals and subsequently recorded the EIS spectra at different appliedbias and illumination intensities to prepare the dataset comprising8741 datapoints. The developed supervised ML model can predict thereal and imaginary parts of the low-frequency EIS response with an R (2) score of 0.981 and a root mean squared error(RMSE) of 0.0196 for the testing set. From the ground truth experimentaldata, it can be observed that negative capacitance prevails at a higherapplied bias. Our developed model can closely predict the real andimaginary parts at a low frequency (50 Hz-300 mHz). Thus, ourmethod makes recording of EIS more accessible and opens a new wayin using the ML techniques for EIS.en_US
dc.description.sponsorshipDean of Scientific Research, King Khalid University [RGP2/354/44]; ORSP of Pandit Deendayal Energy University; DST SERB [IPA/2021/96]; National Science Centre; [2020/39/B/ST5/01497]en_US
dc.description.sponsorshipA.K. is thankful to the Dean of Scientific Research, King Khalid University, for financial support (grant number RGP2/354/44). P.Y. acknowledges the ORSP of Pandit Deendayal Energy University and DST SERB (IPA/2021/96) for financial support. D.P. acknowledges the National Science Centre (Grant OPUS-20, No.2020/39/B/ST5/01497) for financial support. P.Y. also acknowledges Prof Juan Bisquert for technical discussions.en_US
dc.identifier.doi10.1021/acsami.3c00269
dc.identifier.endpage27808en_US
dc.identifier.issn1944-8244
dc.identifier.issn1944-8252
dc.identifier.issue23en_US
dc.identifier.pmid37265458en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage27801en_US
dc.identifier.urihttps://doi.org/10.1021/acsami.3c00269
dc.identifier.urihttps://hdl.handle.net/20.500.12452/12732
dc.identifier.volume15en_US
dc.identifier.wosWOS:001010449400001en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherAmer Chemical Socen_US
dc.relation.ispartofAcs Applied Materials & Interfacesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine Learningen_US
dc.subjectHalide Perovskitesen_US
dc.subjectSinglecrystalsen_US
dc.subjectImpedance Spectroscopyen_US
dc.subjectNegative Capacitanceen_US
dc.subjectLow-Frequency Resistanceen_US
dc.titleProbing the Low-Frequency Response of Impedance Spectroscopy of Halide Perovskite Single Crystals Using Machine Learningen_US
dc.typeArticleen_US

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