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 Gaussian process 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 changes in doping concentration and carrier mobility dominate, while the defect energy level remains nearly unchanged. 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：通过定量实验分析提取钙钛矿材料参数，对光伏与光电应用领域的理性设计至关重要。然而，随着理论模型复杂度的提升及钙钛矿材料参数数量的增加，分析难度显著增大。本文采用高斯过程构建分析平台，基于包含载流子漂移-扩散及动态缺陷占据的完整物理模型，可从瞬态光致发光实验中提取有机金属钙钛矿半导体的最多8个基础材料参数。热降解实例研究表明，掺杂浓度与载流子迁移率的变化起主导作用，而缺陷能级基本保持不变。该平台可便捷应用于其他单一实验或实验组合，加速光伏及其他应用领域半导体材料的发现与优化。