Machine learning approach to identify critical configurations for strong electronic coupling

In an organic semiconductor material, the rate of interchain charge transfer depends on the electronic coupling parameter between neighboring monomers. The rate of charge transfer depends on not only the highest coupling but also the distribution of it. Here, we develop an automated method to calculate electronic coupling that considers the effect of polarization and hence gives a more accurate description. The automatic process allows us to explore various configurations and generate data to train and develop a model using machine learning approaches. Here we have examined the regioregular Poly(3-hexylthiophene) P3HT (donor) as an example. We explore the real structure of P3HT using molecular dynamics simulation. The neighboring P3HT monomer orientations are fed to the machine learning model to obtain electronic coupling distribution. The electronic coupling for an amorphous P3HT system exhibit a skewed distribution with rare but strong coupling above 100 meV. The probability of finding substantial coupling states increases as we anneal the system. The method has also allowed us to obtain the possible key configurations with high electronic coupling.