钙钛矿太阳电池高效光电耦合仿真与机器学习研究(特邀)

Efficient Photoelectric Coupling Simulation and Machine Learning Study of Perovskite Solar Cells(Invited)

十几年来,钙钛矿太阳能电池(PSCs)由于其在功率转换效率和制造成本方面的显著优势而备受关注。然而,其复杂的物理机制和众多限制因素给实验设计、工艺制造和综合优化策略带来挑战。以光电多物理场耦合模型为核心,开展一系列多物理场仿真计算,研究光电耦合模型的底层物理和边界条件,获得PSCs包括光学性能和电学性能的大量数据。根据这些数据,建立微观物理量和宏观光电响应的神经网络及机器学习模型,成功预测PSCs的光学和电学性能,其误差在3%以内,且速度较快。结合遗传算法,该模型根据给定的响应曲线反向优化结构参数,进而获得更高效率的PSCs。该研究有效解决了PSCs因光电耦合机制复杂、物性参数众多、仿真速度较慢而难以优化设计等难题,为光伏器件快速智能化设计提供了一种可行路径。

In recent years,perovskite solar cells(PSCs) have attracted much attention because of their remarkable advantages in power conversion efficiency and manufacturing cost.However,their complex physical mechanisms and numerous constraints pose challenges to experimental design,process fabrication,and comprehensive optimization strategies.Here,we carried out a series of multi-physical field simulations with the optoelectronic multi-physical field coupling model as the core,and studied the underlying physics and boundary conditions of the optoelectronic coupling model,and then obtained a large amount of data on the optical and electrical properties of PSCs.Based on these data,we established the machine learning models and neural network models for the micro physical quantities and macro photoelectric responses,which predicted the performance of PSCs with an error of less than 3% in a fast speed.Combined with the genetic algorithm,the model reversely optimized the structural parameters according to the given response curves to obtain the more efficient PSCs.This study effectively solves the problem that PSCs are difficult to optimize design due to complex photoelectric coupling mechanism,numerous physical property parameters and slow simulation speed,and provides a feasible path for rapid and intelligent design of photovoltaic devices.