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oai:dspace.mit.edu:1721.1/1528052023-11-03T03:52:43Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Bulović, Vladimir Zhang, Ruiqi Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2023-11-02T20:17:50Z 2023-11-02T20:17:50Z 2023-09 2023-09-21T14:26:15.412Z https://hdl.handle.net/1721.1/152805 0000-0003-3901-8737 Organic metal halide perovskites have shown plenty of extraordinary optoelectronic properties which make them good candidates for various photovoltaic applications [1-5]. The fascinating optoelectronic properties of perovskite largely take credit to their low exciton binding energy, strong light absorption coefficient, relatively long carrier diffusion length, and carrier recombination lifetime [6-9]. However, even with an increasing number of studies carried out, perovskite solar cell is still facing plenty of challenges towards commercialization. Two main challenges towards large-area commercialization include first the harsh fabrication environment and cost of large-area coating; and second the redundant fabrication process with a huge labor force impelled. In this thesis study, an intermedia thin film layer tris(4-carbazoyl-9ylphenyl)amine (TcTa) with a thickness of 3 nm is discovered in a large-area compatible perovskite solar cell structure ITO/SnO2/(MAFACs)1Pb(IBrCl)3/PV2000/TcTa/Au that reaches a power conversion efficiency above 14%. The TcTa intermediate film is compatible with substituting gold top electrodes and preventing sputter damage while maintaining a similar solar cell performance (etc. sputtered Ni). In addition, a machine learning algorithm is developed to predict the solar cell current-voltage properties only based on the film stack optical properties before the solar cell is fabricated. The algorithm is developed and tested based on the 3D/2D perovskite solar cell structure [10] with resulting in an average prediction regression loss below 5% and a best prediction accuracy above 99%. Multiple different machine learning algorithm is also carried out to analyze the prediction results and learning weights for the model. S.M. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ Approaching Novel Perovskites Photovoltaic Devices through Machine Learning and Interfacial Engineering Thesis application/pdf Master Master of Science in Electrical Engineering and Computer Science Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="3961f0b8-3ee1-438e-8a9c-8a43b0346dec"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Approaching Novel Perovskites Photovoltaic Devices through Machine Learning and Interfacial Engineering</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2023-09</PublicationDate> <Authors> <Author> <DisplayName>Zhang, Ruiqi</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>Organic metal halide perovskites have shown plenty of extraordinary optoelectronic properties which make them good candidates for various photovoltaic applications [1-5]. The fascinating optoelectronic properties of perovskite largely take credit to their low exciton binding energy, strong light absorption coefficient, relatively long carrier diffusion length, and carrier recombination lifetime [6-9]. However, even with an increasing number of studies carried out, perovskite solar cell is still facing plenty of challenges towards commercialization. Two main challenges towards large-area commercialization include first the harsh fabrication environment and cost of large-area coating; and second the redundant fabrication process with a huge labor force impelled. In this thesis study, an intermedia thin film layer tris(4-carbazoyl-9ylphenyl)amine (TcTa) with a thickness of 3 nm is discovered in a large-area compatible perovskite solar cell structure ITO/SnO2/(MAFACs)1Pb(IBrCl)3/PV2000/TcTa/Au that reaches a power conversion efficiency above 14%. The TcTa intermediate film is compatible with substituting gold top electrodes and preventing sputter damage while maintaining a similar solar cell performance (etc. sputtered Ni). In addition, a machine learning algorithm is developed to predict the solar cell current-voltage properties only based on the film stack optical properties before the solar cell is fabricated. The algorithm is developed and tested based on the 3D/2D perovskite solar cell structure [10] with resulting in an average prediction regression loss below 5% and a best prediction accuracy above 99%. Multiple different machine learning algorithm is also carried out to analyze the prediction results and learning weights for the model.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>