The discovery of high-performance non-fullerene acceptors and ternary blend systems has resulted in a breakthrough in the efficiency of organic photovoltaics (OPVs) and has created new opportunities for commercialization. However, manufacturing technology has remained far behind expectations. Here we show a new research approach to develop OPVs via industrial roll-to-roll (R2R) slot die coating in conjunction with the in situ formulation technique and machine learning (ML) technology. The formulated PM6:Y6:IT-4F ternary blends deposited on continuously moving substrates resulted in the high-throughput fabrication of OPVs with various compositions. The system was used to produce training data for ML prediction. The composition/deposition parameters, referred to as deposition densities, and the efficiencies of 2218 devices were used to screen ML algorithms and to train an ML model based on a Random Forest regression algorithm. The generated model was used to predict high-performance formulations and the prediction was experimentally validated by fabricating 10.2% efficiency devices, the highest efficiency for R2R-processed OPVs so far.

中文翻译：

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机器学习辅助的高通量原位配方开发有机光伏

高性能非富勒烯受体和三元共混体系的发现导致有机光伏（OPV）效率的突破，并为商业化创造了新的机会。但是，制造技术仍然远远落后于预期。在这里，我们展示了一种通过工业卷对卷（R2R）缝口模头涂层与原位结合开发OPV的新研究方法配方技术和机器学习（ML）技术。沉积在连续移动的基材上的配方PM6：Y6：IT-4F三元共混物可高通量制备各种组成的OPV。该系统用于产生用于机器学习预测的训练数据。组成/沉积参数（称为沉积密度）和2218器件的效率用于筛选ML算法并基于随机森林回归算法训练ML模型。生成的模型用于预测高性能配方，并通过制造10.2％的效率器件（目前是R2R处理的OPV的最高效率）通过实验验证了该预测。