Discovery of temperature-induced stability reversal in perovskites using high-throughput robotic learning

• Published in: Nature communications 2021-04, Vol.12 (1), p.2191-2191, Article 2191
• Main Authors: Zhao, Yicheng, Zhang, Jiyun, Xu, Zhengwei, Sun, Shijing, Langner, Stefan, Hartono, Noor Titan Putri, Heumueller, Thomas, Hou, Yi, Elia, Jack, Li, Ning, Matt, Gebhard J., Du, Xiaoyan, Meng, Wei, Osvet, Andres, Zhang, Kaicheng, Stubhan, Tobias, Feng, Yexin, Hauch, Jens, Sargent, Edward H., Buonassisi, Tonio, Brabec, Christoph J.
• Format: Article
• Language: English
• Subjects: 639/301/1005/1007; 639/301/299/946; Accelerated aging tests; Accelerated tests; Aging; Cations; Cesium; Experimentation; High temperature; Humanities and Social Sciences; Learning algorithms; Low temperature; Machine learning; multidisciplinary; Operating temperature; Perovskites; Photovoltaic cells; Photovoltaics; Robot learning; Science; Science (multidisciplinary); Solar cells; Stability analysis; Temperature
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-22472-x

Stability of perovskite-based photovoltaics remains a topic requiring further attention. Cation engineering influences perovskite stability, with the present-day understanding of the impact of cations based on accelerated ageing tests at higher-than-operating temperatures (e.g. 140°C). By coupling high-throughput experimentation with machine learning, we discover a weak correlation between high/low-temperature stability with a stability-reversal behavior. At high ageing temperatures, increasing organic cation (e.g. methylammonium) or decreasing inorganic cation (e.g. cesium) in multi-cation perovskites has detrimental impact on photo/thermal-stability; but below 100°C, the impact is reversed. The underlying mechanism is revealed by calculating the kinetic activation energy in perovskite decomposition. We further identify that incorporating at least 10 mol.% MA and up to 5 mol.% Cs/Rb to maximize the device stability at device-operating temperature (