Predictive Modeling and Design of Organic Solar Cells: A Data-Driven Approach for Material Innovation

We present a robust machine learning
methodology to accurately
predict key photovoltaic parameters in organic solar cells (OSCs).
Our approach involves curating a comprehensive quantum mechanical
database of 300 experimentally validated OSC devices with distinct
donor/acceptor combinations. Through a two-step screening process,
we identify descriptors correlated with crucial properties such as
short-circuit current (JSC), open-circuit
voltage (VOC), fill-factor (FF), and power
conversion efficiency (PCEmax). Utilizing a LASSO model
for feature selection and four different supervised machine learning
techniques for prediction, our model achieves high accuracy, with
gradient boosting showing exceptional performance for JSC, VOC, and PCEmax. Shapley additive explanations (SHAP) analysis reveals the influential
features and the intricate nonlinear relationships governing OSC performance.
Additionally, we extend our model’s utility by predicting photovoltaic
parameters for a larger data set of 4680 donor–acceptor combinations,
including 120 newly designed nonfullerene acceptors and 39 experimentally
known donor polymers. Our results highlight 18 donor–acceptor
combinations with a power conversion efficiency exceeding 15%, emphasizing
the efficacy of our approach in evaluating OSC materials. This work
provides valuable insights for advancing photovoltaic research and
serves as a powerful tool for the virtual screening of promising donor/acceptor
pairs, accelerating the development of high-performance OSC materials
and devices.