The photovoltaic performance of perovskite solar cell is determined by multiple interrelated factors, such as perovskite compositions, electronic properties of each transport layer and fabrication parameters, which makes it rather challenging for optimization of device performances and discovery of underlying mechanisms. Here, we propose and realize a novel machine learning approach based on forward-reverse framework to establish the relationship between key parameters and photovoltaic performance in high-profile MASn_xPb_1-xI₃ perovskite materials. The proposed method establishes the asymmetrically bowing relationship between band gap and Sn composition, which is precisely verified by our experiments. Based on the analysis of structural evolution and SHAP library, the rapid-change region and low-bandgap plateau region for small and large Sn composition are explained, respectively. By establishing the models for photovoltaic parameters of working photovoltaic devices, the deviation of short-circuit current and open-circuit voltage with band gap in defective-zone and low-bandgap-plateau regions from Shockley-Queisser theory is captured by our models, and the former is due to the deep-level traps formed by crystallographic distortion and the latter is due to the enhanced susceptibility by increased Sn⁴⁺ content. The more difficulty for hole extraction than electron is also concluded in the models and the prediction curve of power conversion efficiency is in a good agreement with Shockley-Queisser limit. With the help of search and optimization algorithms, an optimized Sn:Pb composition ratio near 0.6 is finally obtained for high-performance perovskite solar cells, then verified by our experiments. Our constructive method could also be applicable to other material optimization and efficient device development.

钙钛矿太阳能电池的光伏性能由多个相互关联的因素决定，例如钙钛矿成分、每个传输层的电子特性和制造参数，这使得优化器件性能和发现潜在机制变得相当具有挑战性。在这里，我们提出并实现了一种基于正反向框架的新型机器学习方法，以建立高调 MASn _x Pb _1-x I _{3中关键参数与光伏性能之间的关系}钙钛矿材料。所提出的方法建立了带隙和Sn成分之间的不对称弯曲关系，我们的实验精确地验证了这一点。基于结构演化和SHAP库分析，分别解释了小Sn成分和大Sn成分的快速变化区和低带隙平台区。通过建立工作光伏器件的光伏参数模型，我们的模型捕获了短路电流和开路电压在缺陷区和低带隙平台区的带隙与肖克利-奎塞尔理论的偏差，并且前者是由于晶体畸变形成的深能级陷阱，后者是由于增加的 Sn ^{4+提高了磁化率}内容。模型中还得出空穴提取难度大于电子提取的结论，功率转换效率的预测曲线与Shockley-Queisser极限吻合较好。在搜索和优化算法的帮助下，最终为高性能钙钛矿太阳能电池获得了接近 0.6 的优化 Sn:Pb 组成比，然后通过我们的实验验证。我们的建设性方法也适用于其他材料优化和高效设备开发。