Predicting UV-Vis Spectra of Benzothio/Dithiophene Polymers for Photodetectors by Machine-Learning-Assisted Computational Studies

The current study represents a machine-learning (ML)-assisted reverse polymer engineering for the rational design of high-performance benzothiophene (BT) benzodithiophene (BDT) polymers for photodetector applications. By integrating their 5617 units with various acceptor moieties, a total of 72,976 unique polymer combinations are generated. The optical properties of these polymers are predicted with high accuracy (R2 = 0.86) using a Gradient-Boosting Regression (GBR) model. The SHAP value-based feature importance analysis indicates that Chi0 is the most influential factor in predicting the absorption maxima (λmax) of polymers, followed by LabuteASA, Chi0V, Chi1, SlogP_VSA12, and other molecular descriptors. The robustness of the employed model is further validated through K-Fold cross-validation, with the highest mean squared error (MSE) observed at 2.02 in the fold-2 subset. The designed polymers exhibit λmax within the range of 400–750 nm, demonstrating their suitability for photodetector applications. Moreover, a Transformer-Assisted Orientation (TAO) approach is employed to optimize polymer design, successfully achieving bandgaps as low as 0.42 eV. This approach facilitates the rapid design and optimization of high-performance polymers with tailored electronic properties, effectively addressing the limitations of conventional trial-and-error methods. The current ML-assisted approach presents a promising strategy for expediting the development of high-performance photodetectors and other advanced optoelectronic devices.