Mass-customization of organic photovoltaics and data production for machine learning models precisely predicting device behavior

Abstract

High-throughput experimentation (HTE) combined with machine learning (ML) has emerged as a powerful tool to accelerate material discovery or optimize fabrication processes. However, in the photovoltaics field, only a few studies have successfully applied this approach using industrially relevant techniques, such as the roll-to-roll (R2R) process. We developed a universal and extendable data structure for ML training that accommodates upcoming materials, while retaining compatibility with the existing dataset. Using the MicroFactory platform, which enables mass-customization of organic photovoltaics (OPVs), we fabricated and characterized over 26 000 unique cells within four days. To guide the selection of the ML model for precisely predicting device behavior, photovoltaic parameter and J–V prediction models to forecast device parameters and J–V curves, respectively, were developed. The Random Forest model proved to be the most effective, achieving a PCE of 11.8% (0.025 cm²)—the highest for a fully-R2R-fabricated OPV. By integrating accumulated datasets with smaller new-component datasets, we enhanced model performance for PM6:Y6:IT-4F and PM6:D18:L8-BO systems, showing that models trained on binary systems can predict ternary device performance and enabling the development of generalized ML models for future high-performance materials.