{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/152805"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/152805","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Approaching Novel Perovskites Photovoltaic Devices through Machine Learning and Interfacial Engineering","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_html":"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_has_math":false,"creators":["Zhang, Ruiqi"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Approaching Novel Perovskites Photovoltaic Devices through Machine Learning and Interfacial Engineering"]}]}],"canonical_facts":{"dc:contributor.advisor":["Bulović, Vladimir"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Zhang, Ruiqi"],"dc:date.accessioned":["2023-11-02T20:17:50Z"],"dc:date.available":["2023-11-02T20:17:50Z"],"dc:date.issued":["2023-09"],"dc:description.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."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/152805"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Approaching Novel Perovskites Photovoltaic Devices through Machine Learning and Interfacial Engineering"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Science in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:21:09Z"}