The ability to extract material parameters from quantitative experimental analysis is essential for rational design and theory advancement. However, the difficulty of this analysis increases significantly with the complexity of the theoretical model and the number of material parameters. 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 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：从定量实验分析中提取材料参数对于理性设计和理论发展至关重要。然而，随着理论模型复杂度和材料参数数量的增加，这种分析的难度显著提升。本研究采用贝叶斯优化方法构建了一个分析平台，能够基于包含载流子漂移扩散和动态缺陷占据的完整物理模型，从瞬态光致发光实验中提取有机金属钙钛矿半导体的多达8个基本材料参数。一项关于热降解的案例研究表明，掺杂浓度和载流子迁移率的变化占主导地位，而缺陷能级几乎保持不变。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。