Beyond molecular structure: critically assessing machine learning for designing organic photovoltaic materials and devices

Our study explores the current state of machine learning (ML) as applied to predicting and designing organic photovoltaic (OPV) devices. We outline key considerations for selecting the method of encoding a molecular structure and selecting the algorithm while also emphasizing important aspects of training and rigorously evaluating ML models. This work presents the first comprehensive dataset of OPV device fabrication data mined from the literature. The top models achieve state-of-the-art predictive performance. In particular, we identify an algorithm that is used less frequently, but may be particularly well suited to similar datasets. However, predictive performance remains modest ( R 2 ≅ 0.6) overall. An in-depth analysis of the dataset attributes this limitation to challenges relating to the size of the dataset, as well as data quality and sparsity. These aspects are directly tied to difficulties imposed by current reporting and publication practices. Advocating for standardized reporting of OPV device fabrication data reporting in publications emerges as crucial to streamline literature mining and foster ML adoption. This comprehensive investigation emphasizes the critical role of both data quantity and quality, and highlights the need for collective efforts to unlock ML's potential to drive advancements in OPV. We assess state of machine learning for organic photovoltaic devices and data availability within the field, discuss best practices in representations and model selection, and release a comprehensive dataset of devices and fabrication conditions.