The ability to extract material parameters of perovskite from quantitative experimental analysis is essential for rational design of photovoltaic and optoelectronic applications. However, the difficulty of this analysis increases significantly with the complexity of the theoretical model and the number of material parameters for perovskite. Here we use Bayesian optimization to develop an analysis platform that can extract up to 8 fundamental material parameters of an organometallic perovskite semiconductor from a transient photoluminescence experiment, based on a complex full physics model that includes drift-diffusion of carriers and dynamic defect occupation. An example study of thermal degradation reveals that the carrier mobility and trap-assisted recombination coefficient are reduced noticeably, while the defect energy level remains nearly unchanged. The reduced carrier mobility can dominate the overall effect on thermal degradation of perovskite solar cells by reducing the fill factor, despite the opposite effect of the reduced trap-assisted recombination coefficient on increasing the fill factor. In future, this platform can be conveniently applied to other experiments or to combinations of experiments, accelerating materials discovery and optimization of semiconductor materials for photovoltaics and other applications.

翻译：从定量实验分析中提取钙钛矿材料参数的能力对于光伏和光电器件的理性设计至关重要。然而，随着理论模型的复杂性和钙钛矿材料参数数量的增加，该分析的难度显著提高。本文利用贝叶斯优化开发了一个分析平台，该平台基于包含载流子漂移扩散和动态缺陷占据的复杂全物理模型，能够从瞬态光致发光实验中提取有机金属钙钛矿半导体多达8个基本材料参数。一项热降解案例研究表明，载流子迁移率和陷阱辅助复合系数显著降低，而缺陷能级几乎保持不变。尽管陷阱辅助复合系数降低对提升填充因子有相反作用，但载流子迁移率的降低会主导钙钛矿太阳能电池热降解的整体效应，主要通过降低填充因子实现。未来，该平台可便捷地应用于其他实验或实验组合，加速光伏及其他应用领域半导体材料的发现与优化进程。