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、每个传输层的电子性能和制造参数，这使得器件性能的优化和潜在机制的发现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向-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钙钛矿材料中关键参数与光伏性能之间的关系。所提出的方法建立了带隙和Sn成分之间的不对称弓形关系，我们的实验精确地验证了这一关系。基于结构演化和SHAP库的分析，分别解释了小锡成分和大锡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，我们的模型捕获了缺陷区和低带隙平台区域的短路电流和开路电压随带隙的偏差与Shockley-Queisser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Sn ⁴⁺含量增加而增强的磁化率。模型中还得出了空穴提取比电子更困难的结论，并且功率转换效率的预测曲线与Shockley-Queisser极限吻合良好。在搜索和优化算法的帮助下，最终获得了接近0.6的高性能钙钛矿太阳能电池的优化Sn:Pb成分比，并通过实验进行了验证。 我们的构造方法也适用于其他材料优化和高效设备开发。