Cha Jeongbeom, Baek Dohun, Jin Haedam, Na Hyemi, Park Geon Yeong, Ham Dong Seok, Kim Min
Graduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju 54896, Republic of Korea.
School of Chemical Engineering, Clean Energy Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea.
ACS Omega. 2023 Oct 26;8(44):41558-41569. doi: 10.1021/acsomega.3c05622. eCollection 2023 Nov 7.
Organic-inorganic metal halide perovskite solar cells are renowned for their extensive solution processability, although the production of uniformly crystalline perovskite films can necessitate intricate deposition methods. In our study, we harmonized Shockley diode-based numerical analysis with machine learning techniques to extract the device characteristics of perovskite solar cells and optimize their photovoltaic performance in light of the experimental variables. The application of the Shockley diode equation facilitated the extraction of photovoltaic parameters and the prediction of power conversion efficiencies, thus aiding the understanding of device physics and charge recombination. Through machine learning, specifically Gaussian process regression, we trained models on current-voltage curves sensitive to variations in fabrication conditions, thereby pinpointing the optimal settings for enhanced device performance. Our multifaceted approach not only clarifies the interplay between experimental conditions and device performance but also streamlines the optimization process, diminishing the need for exhaustive trial-and-error experiments. This methodology holds substantial promise for advancing the development and fine-tuning of next-generation perovskite solar cells.
有机-无机金属卤化物钙钛矿太阳能电池以其广泛的溶液可加工性而闻名，尽管制备均匀结晶的钙钛矿薄膜可能需要复杂的沉积方法。在我们的研究中，我们将基于肖克利二极管的数值分析与机器学习技术相结合，以提取钙钛矿太阳能电池的器件特性，并根据实验变量优化其光伏性能。肖克利二极管方程的应用有助于提取光伏参数并预测功率转换效率，从而有助于理解器件物理和电荷复合。通过机器学习，特别是高斯过程回归，我们在对制造条件变化敏感的电流-电压曲线上训练模型，从而确定提高器件性能的最佳设置。我们的多方面方法不仅阐明了实验条件与器件性能之间的相互作用，还简化了优化过程，减少了进行详尽的反复试验实验的必要性。这种方法对于推进下一代钙钛矿太阳能电池的开发和微调具有巨大的前景。
