Machine Learning for Structure-Performance Relationships in Organic Semiconducting Devices

Organic semiconducting materials have the potential to provide an inexpensive and tunable alternative to conventional inorganic materials for use in the construction of electronic devices. The performance of these devices depends on the movement of charges through the fine intermolecular structure. Computational methods can predict these structures and subsequent electronic properties for the wide variety of candidate molecules, however, it is too computationally expensive to calculate the properties for the many combinations of molecules. To overcome this, we develop machine learning tools to predict electronic properties and bypass the computational bottlenecks thereby enabling a widespread investigation of the variables affecting device performance. Presented here is the database of intermolecular structures and corresponding electronic properties used in training our machine learning algorithms for the molecules dibenzo-tetraphenyl-periflanthene, benzo-dithiophene-thienopyrrolodione, and poly-3-hexylthiophene