Fusion Deep Learning for Predicting Conductivity in Electron-Doped Organic Polymers

The development of efficient and air-stable n-type organic semiconductors suitable for molecular n-doping is critical for advancing high-performance, durable organic electronic devices, including transistors, thermoelectrics, and photovoltaics. Machine learning offers a powerful approach to uncovering hidden relationships between molecular structures and their electronic properties, thereby accelerating the discovery and design of promising materials. To support this effort, we constructed a curated database comprising 84 n-type conductive polymers, each characterized by experimental measurements under n-doping conditions with 4-(1,3-dimethyl-2,3-dihydro-1<i>H</i>-benzoimidazol-2-yl)phenyl dimethylamine (N-DMBI-H), and augmented with density functional theory calculations to provide complementary molecular descriptors. After constructing the database, we developed a fusion deep learning model that integrates convolutional neural networks with fully connected artificial neural networks to capture both structural and property-based features of the polymers. The model was trained on the data set and evaluated using leave-one-out cross-validation. The model was further applied to a test set of n-type polymers bearing oligoethylene glycol (OEG) side chains, enabling the identification of key physical factors that influence their conductivity when doped with N-DMBI-H. Finally, a double-blind experiment was conducted to validate the model's practical utility by predicting the conductivity of four BDPPV-type polymers and one N2200-type polymer doped with N-DMBI-H. For the N2200-type polymer and two of the BDPPV-type polymers, the predicted conductivities agreed with experimental values within the same order of magnitude. These results demonstrate the fusion model's reliability and establish a strong foundation for data-driven property prediction and the design of high-conductivity n-type polymers.