Department of Chemistry, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar 382 007, Gujarat, India.
Department of Metallurgical and Materials Engineering, Karamanoglu Mehmetbey University, Karaman 70200, Turkey.
ACS Appl Mater Interfaces. 2023 Jun 14;15(23):27801-27808. doi: 10.1021/acsami.3c00269. Epub 2023 Jun 2.
Electrochemical impedance spectroscopy (EIS) has emerged as a versatile technique for characterization and analysis of metal halide perovskite solar cells (PSCs). The crucial information about ion migration and carrier accumulation in PSCs can be extracted from the low-frequency regime of the EIS spectrum. However, lengthy measurement time at low frequencies along with material degradation due to prolonged exposure to light and bias motivates the use of machine learning (ML) in predicting the low-frequency response. Here, we have developed an ML model to predict the low-frequency response of the halide perovskite single crystals. We first synthesized high-quality MAPbBr single crystals and subsequently recorded the EIS spectra at different applied bias and illumination intensities to prepare the dataset comprising 8741 datapoints. The developed supervised ML model can predict the real and imaginary parts of the low-frequency EIS response with an score of 0.981 and a root mean squared error (RMSE) of 0.0196 for the testing set. From the ground truth experimental data, it can be observed that negative capacitance prevails at a higher applied bias. Our developed model can closely predict the real and imaginary parts at a low frequency (50 Hz-300 mHz). Thus, our method makes recording of EIS more accessible and opens a new way in using the ML techniques for EIS.
电化学阻抗谱 (EIS) 已成为一种用于卤化物钙钛矿太阳能电池 (PSC) 特性分析和研究的多功能技术。通过 EIS 光谱的低频部分可以提取出有关 PSC 中离子迁移和载流子积累的关键信息。然而，低频测量需要较长的时间，并且长时间暴露在光和偏压下会导致材料降解，这促使人们使用机器学习 (ML) 来预测低频响应。在这里，我们开发了一种 ML 模型来预测卤化物钙钛矿单晶的低频响应。我们首先合成了高质量的 MAPbBr 单晶，然后在不同的外加偏压和光照强度下记录 EIS 谱，以准备包含 8741 个数据点的数据集。所开发的监督 ML 模型可以预测低频 EIS 响应的实部和虚部，对于测试集，得分为 0.981，均方根误差 (RMSE) 为 0.0196。从真实的实验数据可以观察到，在较高的外加偏压下存在负电容。我们开发的模型可以在低频（50 Hz-300 mHz）下很好地预测实部和虚部。因此，我们的方法使 EIS 的记录变得更加容易，并为使用 ML 技术进行 EIS 开辟了新途径。
