Data-driven design of polar organic semiconductors: A terminal group engineering approach

Organic semiconductors (OSCs) have attracted attentions of researchers due to their latent uses in a variety of electronic devices. Enhancing the dipole moment of OSCs is critical for improving device performance. In order to create OSCs with larger dipole moments, multiple terminal groups structural strategy is good option. Our goal is to raise the total dipole moment of the OSCs by carefully adding two or more electron-accepting terminal groups to the molecular structure. To predict the dipole moment, large number of machine learning models are trained. A large database of new semiconductors is created. Using cluster plot and heatmap, the thirty semiconductors' chemical similarity was further examined. This study is introducing easy and fast framework for the designing of efficient materials for organic electronic devices by offering insightful information about the rational design of OSCs with improved dipole moments.