Machine learning-guided scalable manufacturing of high-efficiency perovskite solar modules

Based on an XGBoost multi-target prediction model, this code optimizes the device parameters for perovskite solar cells. By performing a grid search across the combinatorial space of cation (Cs/FA/MA) and halide (Br/I/Cl) components, it identifies the optimal formulation while keeping the device structural parameters fixed; furthermore, it applies a correction function to calibrate the predicted efficiency based on specific processing conditions. Additionally, for a given fabrication process and fixed composition, the code scans and plots the efficiency response curve as a function of device area. This code constructs a four-target prediction model for perovskite solar cells based on XGBoost. The data preprocessing module normalizes the cationic (Cs/FA/MA) and halide anionic (Br/I/Cl) components, applies one-hot encoding to categorical variables—such as additives and passivators—and imputes missing values in numerical features using the median. The model employs an XGBoost regressor with fixed hyperparameters for training. For efficiency prediction, the code reads new device parameters from an external file, automatically imputes any missing feature columns, and then invokes the PCE model to perform inference, thereby enabling the rapid prediction of the Power Conversion Efficiency (PCE) for unknown samples. This code constructs a multi-objective predictive model for perovskite solar cells based on XGBoost, performing regression predictions for PCE, Jsc, and FF, and a three-class classification prediction for Voc. It mitigates data imbalance issues through 2D joint sample weighting and enhances predictive accuracy by combining RandomizedSearchCV-based hyperparameter optimization with isotonic regression post-processing. Furthermore, SHAP analysis is introduced to quantify the feature contributions for the four target variables, while a material-grouping SHAP analysis is employed to reveal the influence patterns of the materials within each functional layer.本代码基于XGBoost多目标预测模型，对钙钛矿太阳能电池的器件参数进行优化。通过在阳离子（Cs/FA/MA）与卤化物（Br/I/Cl）组分的组合空间中执行网格搜索，在固定器件结构参数的前提下确定最优配方；此外，本代码还基于特定制备条件应用校正函数对预测的器件效率进行校准。此外，针对给定的制备工艺与固定组分，代码可扫描并绘制出以器件面积为变量的效率响应曲线。 本代码基于XGBoost构建了钙钛矿太阳能电池的四目标预测模型。其数据预处理模块会对阳离子（Cs/FA/MA）与卤化物阴离子（Br/I/Cl）组分进行归一化处理，对添加剂、钝化剂等分类变量执行独热编码，并采用中位数插补数值特征中的缺失值。模型采用固定超参数的XGBoost回归器进行训练。在进行效率预测时，代码会从外部文件读取新的器件参数，自动补全缺失的特征列后调用光电转换效率（Power Conversion Efficiency, PCE）模型进行推理，从而实现对未知样本的光电转换效率快速预测。 本代码基于XGBoost构建钙钛矿太阳能电池多目标预测模型，可对光电转换效率（PCE）、短路电流密度（Short-Circuit Current Density, Jsc）、填充因子（Fill Factor, FF）开展回归预测，并对开路电压（Open-Circuit Voltage, Voc）进行三类分类预测。代码通过二维联合样本加权缓解数据不平衡问题，并结合基于RandomizedSearchCV的超参数优化与保序回归后处理提升预测精度。此外，本代码引入SHAP（SHapley Additive exPlanations）分析以量化四个目标变量的特征贡献度，并采用材料分组SHAP分析揭示各功能层内材料的影响规律。