Capping Layers Design Guidelines for Stable Perovskite Solar Cells via Machine Learning
Name
1767417.pdf
Description
Accepted version
Size
257.37 KB
Format
Unknown
Checksum (MD5)
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Author(s)
Hartono, Noor Titan Putri
Thapa, Janak
Tiihonen, Armi
Oviedo, Felipe
Batali, Clio
Yoo, Jason Jungwan
Liu, Zhe
Li, Ruipeng
Marron, David Fuertes
Bawendi, Moungi G
Date Issued
2020
Journal
Conference Record of the IEEE Photovoltaic Specialists Conference
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Hartono, Noor Titan Putri, Thapa, Janak, Tiihonen, Armi, Oviedo, Felipe, Batali, Clio et al. 2020. "Capping Layers Design Guidelines for Stable Perovskite Solar Cells via Machine Learning." Conference Record of the IEEE Photovoltaic Specialists Conference, 2020-June.
Version
Author's final manuscript
Abstract
© 2020 IEEE. After reaching a device efficiency level comparable to silicon, perovskite solar cell's next big challenge is to tackle its environmental instability issue. To solve this problem, researchers have started incorporating a buffer layer called 'capping layer', consisting of low dimensional (LD) perovskite, sandwiched between perovskite absorber and hole transport layer. However, there is no conclusive agreement on how to select capping layer material that best extends the stability. By using feature importance rank on the regression models, we can start to see which molecular properties on capping layer have significant impact in suppressing degradation.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/PVSC45281.2020.9300622
