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
5-18-2018
DOI
https://doi.org/10.18122/cme_lab/3/boisestate
Funding Citation
This material is based upon work supported by the National Science Foundation under Grant No. 1653954 and 1229709.
Single Dataset or Series?
Single Dataset
Data Format
Python pickle files and sql databases; *.gz
Data Attributes
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.
Update Frequency
Other
Privacy and Confidentiality Statement
Boise State is explicitly compliant with federal and state laws surrounding data privacy including the protection of personal financial information through the Gramm-Leach-Bliley Act, personal medical information through HIPAA, HITECH and other regulations. All human subject data (e.g., surveys) has been collected and managed only by personnel with adequate human subject protection certification.
Use Restrictions
Users are free to share, copy, distribute and use the dataset; to create or produce works from the dataset; to adapt, modify, transform and build upon the dataset as long as the user attributes any public use of the dataset, or works produced from the dataset, referencing the author(s) and DOI link. For any use or redistribution of the dataset, or works produced from it, the user must make clear to others the license of the dataset and keep intact any notices on the original dataset.
Disclaimer of Warranty
BOISE STATE UNIVERSITY MAKES NO REPRESENTATIONS ABOUT THE SUITABILITY OF THE INFORMATION CONTAINED IN OR PROVIDED AS PART OF THE SYSTEM FOR ANY PURPOSE. ALL SUCH INFORMATION IS PROVIDED "AS IS" WITHOUT WARRANTY OF ANY KIND. BOISE STATE UNIVERSITY HEREBY DISCLAIMS ALL WARRANTIES AND CONDITIONS WITH REGARD TO THIS INFORMATION, INCLUDING ALL WARRANTIES AND CONDITIONS OF MERCHANTABILITY, WHETHER EXPRESS, IMPLIED OR STATUTORY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT.
IN NO EVENT SHALL BOISE STATE UNIVERSITY BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF INFORMATION AVAILABLE FROM THE SYSTEM.
THE INFORMATION PROVIDED BY THE SYSTEM COULD INCLUDE TECHNICAL INACCURACIES OR TYPOGRAPHICAL ERRORS. CHANGES ARE PERIODICALLY ADDED TO THE INFORMATION HEREIN. COMPANY AND/OR ITS RESPECTIVE SUPPLIERS MAY MAKE IMPROVEMENTS AND/OR CHANGES IN THE PRODUCT(S) AND/OR THE PROGRAM(S) DESCRIBED HEREIN AT ANY TIME, WITH OR WITHOUT NOTICE TO YOU.
BOISE STATE UNIVERSITY DOES NOT MAKE ANY ASSURANCES WITH REGARD TO THE ACCURACY OF THE RESULTS OR OUTPUT THAT DERIVES FROM USE OF THE SYSTEM.
Recommended Citation
Miller, Evan; Jones, Matthew L.; and Jankowski, Eric, "Machine Learning for Structure-Performance Relationships in Organic Semiconducting Devices" (2018). Computational Materials Engineering Laboratory. 3.
https://scholarworks.boisestate.edu/cme_lab/3
Comments
Changes made on May 24, 2018:
1) Added replicant runs for each P3HT morphology containing 1000 chains
2) Added both annealed and quenched BDT-TPD morphologies
3) Updated all pickle data files to be compatible with the latest version of MorphCT (v3.0.1)