%0 Journal Article
%A Zhao, Yicheng
%A Zhang, Jiyun
%A Xu, Zhengwei
%A Sun, Shijing
%A Langner, Stefan
%A Hartono, Noor Titan Putri
%A Heumueller, Thomas
%A Hou, Yi
%A Elia, Jack
%A Li, Ning
%A Matt, Gebhard J.
%A Du, Xiaoyan
%A Meng, Wei
%A Osvet, Andres
%A Zhang, Kaicheng
%A Stubhan, Tobias
%A Feng, Yexin
%A Hauch, Jens
%A Sargent, Edward H.
%A Buonassisi, Tonio
%A Brabec, Christoph J.
%T Discovery of temperature-induced stability reversal in perovskites using high-throughput robotic learning
%J Nature Communications
%V 12
%N 1
%@ 2041-1723
%C [London]
%I Nature Publishing Group UK
%M FZJ-2021-02199
%P 2191
%D 2021
%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 (<100°C). We close by demonstrating the methylammonium-containing perovskite solar cells showing negligible efficiency loss compared to its initial efficiency after 1800 hours of working under illumination at 30°C.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ 33850155
%U <Go to ISI:>//WOS:000640638000003
%R 10.1038/s41467-021-22472-x
%U https://juser.fz-juelich.de/record/892610