通过神经网络方法加速钙钛矿太阳能电池的器件表征

Abstract Perovskite solar cells are promising candidates for next-generation high-efficiency photovoltaic devices , especially as top cells in tandem applications. Using a physical-based optoelectronic model, we collect big data of one hundred thousand sample size to train neural network models for efficient prediction of device performance and recombination losses . Latin hypercube sampling , Bayesian regularization , and Bayesian optimization are adopted for data preparation, model training, and optimization of the neural networks, respectively. The best neural network models achieved mean squared errors below 4 × 10 − 4 on a reserved testing dataset . The computational speed of the neural network is more than one thousand times faster than traditional optoelectronic models. As a result, fast device calibration can be conducted in twenty-four seconds. The reduced computational cost allows for efficient device characterization, parametric studies , sensitivity analysis, loss analysis, and optimization. After optimizing interface recombination in our in-house fabricated devices, we observed an experimental improvement of approximately 2 % in power conversion efficiency . Additionally, we predict theoretical power conversion efficiencies of 28.9 % and 25.5 % for perovskite solar cells with band gaps of 1.56 eV and 1.63 eV, respectively.