Capping Layers Design Guidelines for Stable Perovskite Solar Cells via Machine Learning

• Main Authors: Hartono, Noor Titan Putri (Author), Thapa, Janak (Author), Tiihonen, Armi (Author), Oviedo, Felipe (Author), Batali, Clio (Author), Yoo, Jason Jungwan (Author), Liu, Zhe (Author), Li, Ruipeng (Author), Marron, David Fuertes (Author), Bawendi, Moungi G (Author), Buonassisi, Anthony (Author), Sun, Shijing (Author)
• Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers (IEEE), 2022-01-05T20:52:01Z.
• Subjects: Article
• Online Access: Get fulltext

© 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.
NASA (Grant NNX16AM70H)