Summary & Purpose
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.
Date of Publication or Submission
This material is based upon work supported by the National Science Foundation under Grant No. 1653954 and 1229709.
Single Dataset or Series?
Python pickle files and sql databases; *.gz
The python pickle files comprise data generated from molecular dynamic and kinetic Monte Carlo simulations. These pickle files contain the atomic positions and electronic properties for every pair of neighboring molecules. This data was generated with the MorphCT package: https://bitbucket.org/cmelab/morphct/. The sql files contain tables for each pickle file. Within each table is the calculated distance, orientational and energetic differences between the pairs and their resultant transfer integral -- the calculated overlap of the electron orbitals.
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Miller, Evan; Jones, Matthew L.; and Jankowski, Eric, "Machine Learning for Structure-Performance Relationships in Organic Semiconducting Devices" (2018). Computational Materials Engineering Laboratory. 3.