Design and prediction of high optical density photovoltaic polymers using machine learning-DFT studies

Abstract This research provides a machine learning (ML) method to predict the optical density of photovoltaic (PV) polymers. The results show that extra gradient boosting regressor and random forest regressor are the best-performing models among all the tested ML models, whose good R -squared ( R 2 ) values reveal their prediction accuracy. Furthermore, their SHapley Additive exPlanations analysis demonstrates that the polar surface area is the most impactful feature to influence its model outputs for highlighting its essential role in their design process. The results also reveal that the evaluated models exhibit consistent performance across different K values, with their mean squared error values spanning from 0.001 to 0.009 and R 2 values of 0.96–0.98. In addition, their synthetic accessibility likelihood index scores show a value as high as 15 for the top polymers to imply favorable conditions for their practical synthesis. The current study not only expands the understanding of polymer design but also furthers the field of PVs by establishing a good framework to design efficient solar energy materials.