Evaluating the potential of organic photovoltaic (OPV) materials and devices for industrial production is a multidimensional optimization process with an incredibly large parameter space. Here, we demonstrate automated OPV material and device characterization in terms of efficiency and photostability. Gaussian process regression (GPR) prediction based on optical absorption features guided the optimization process with promising prediction accuracy for PV parameters and burn-in losses. With ∼100 process conditions, screening for efficiency and photostability can be finished within 70 h. The highest power conversion efficiency (PCE) of 14% was achieved by fully automated device fabrication in air with a model material system PM6:Y6. Improving molecular ordering has been identified as the most promising motif for further efficiency optimization. Thin active layers combined with medium thermal annealing temperature are favorable to simultaneously improve efficiency and suppress burn-in losses. The platform and protocol may be expanded to any solution-processed organic semiconductor and interface materials.

评估有机光伏（OPV）材料和用于工业生产的设备的潜力是一个多维优化过程，具有极大的参数空间。在这里，我们展示了效率和光稳定性方面的自动OPV材料和设备表征。基于光吸收特征的高斯过程回归（GPR）预测以具有前景的PV参数和老化损耗预测精度指导了优化过程。在约100个工艺条件下，可以在70小时内完成对效率和光稳定性的筛选。通过使用模型材料系统PM6：Y6在空气中进行全自动设备制造，可以实现14％的最高功率转换效率（PCE）。改善分子有序性已被确定为进一步效率优化的最有希望的主题。薄的有源层与中等的热退火温度相结合，有利于同时提高效率并抑制老化损耗。平台和协议可以扩展到任何溶液处理的有机半导体和界面材料。