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

Yicheng Zhao*, Jiyun Zhang, Zhengwei Xu, Shijing Sun, Stefan Langner, Noor Titan Putri Hartono, Thomas Heumueller, Yi Hou, Jack Elia, Ning Li, Gebhard J. Matt, Xiaoyan Du, Wei Meng, Andres Osvet, Kaicheng Zhang, Tobias Stubhan, Yexin Feng, Jens Hauch, Edward H. Sargent, Tonio BuonassisiChristoph J. Brabec

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

86 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number2191
JournalNature communications
Volume12
Issue number1
DOIs
StatePublished - Dec 1 2021

ASJC Scopus subject areas

  • General Chemistry
  • General Biochemistry, Genetics and Molecular Biology
  • General Physics and Astronomy

Fingerprint

Dive into the research topics of 'Discovery of temperature-induced stability reversal in perovskites using high-throughput robotic learning'. Together they form a unique fingerprint.

Cite this