基于物理的机器学习分析钙钛矿太阳能电池的退化
Analysis of degradation in perovskite solar cells through physics-based machine learning
AI总结：
本研究结合机器学习与IonMonger模拟，分析钙钛矿太阳能电池退化，明确其受可移动离子浓度、扩散系数相关变化及界面复合影响，为可靠解释实验结果提供有效方法。
AI中文摘要：
通过对已发表的单钙钛矿太阳能电池在0、90、280、480分钟时的特性测量数据进行反演建模，分析铅卤化物钙钛矿太阳能电池的退化。我们采用机器学习推导材料参数值的分布，进而推导与测量变化相关的物理机制。将贝叶斯参数估计与IonMonger代码的漂移扩散模拟相结合，同时辅以光学模型。我们通过模型输入参数的变化，准确复现了器件性能随时间的测量变化。本研究的关键结果是，退化受可移动离子的浓度与扩散系数的相关变化，以及高可移动离子浓度下的界面复合影响。该研究证明了机器学习与模拟结合的强大能力，可可靠地解释实验结果，而仅通过手动探索输入参数空间的模拟模型难以完成这一任务。
英文摘要：
Degradation in lead halide perovskite solar cells is analysed by inverse modelling of published measurements of characteristics of a single solar cell at ages 0, 90, 280, 480 minutes. We employ machine learning to deduce distributions of material parameter values and hence the physics linked to measured changes. Bayesian parameter estimation is coupled with drift diffusion simulations using the IonMonger code combined with an optical model. We accurately replicated measured changes in device performance with age through variations in model input parameters. Our key result is that degradation is influenced by correlated changes in the concentrations and diffusion coefficients of mobile ions and by interface recombination at large mobile ion concentrations. This study demonstrates the power of machine learning combined with simulations to reliably interpret experimental results, a task which is problematic if using simulation models with only manual exploration of the input parameter space.
