Physics-Informed Machine Learning with Data-Driven Equations for Predicting Organic Solar Cell Performance

Organic solar cells (OSCs) have emerged as a promising
solution
in pursuing sustainable energy. This study presents a comprehensive
approach to advancing OSC development by integrating data-driven equations
from quantum mechanical (QM) descriptors with physics-informed machine
learning (PIML) models. We circumvent traditional experimental limitations
through high-throughput QM calculations, prioritizing transparent
and interpretable models. Using the SISSO++ method, we identified
key descriptors that effectively map the relationships between input
variables and photovoltaic performance metrics. Our innovative predictive
models, derived from SISSO outputs, excel in forecasting critical
OSC parameters such as short-circuit current (JSC), open-circuit voltage (VOC),
fill factor (FF), and power conversion efficiency (PCEmax), achieving high accuracy even with limited data sets. To validate
our models’ practical utility, we applied the PIML framework
to a newly compiled data set of OSC devices, demonstrating their versatility
and capability in pinpointing high-performance materials. This research
underscores the strong predictive power of our models, bridging the
gap between experimental results and theoretical predictions and making
significant contributions to the advancement of sustainable energy
technologies.