Machine Learning Predictions Electronic Couplings for Charge Transport Calculations of P3HT

Authors

Evan D. Miller, Boise State University
Matthew L. Jones, Boise State University
Mike M. Henry, Boise State University
Bryan Stanfill, Pacific Northwest National Laboratory
Eric Jankowski, Boise State UniversityFollow

Document Type

Article

Publication Date

12-2019

Abstract

The purpose of this work is to lower the computational cost of predicting charge mobilities in organic semiconductors, which will benefit the screening of candidates for inexpensive solar power generation. We characterize efforts to minimize the number of expensive quantum chemical calculations we perform by training machines to predict electronic couplings between monomers of poly-(3-hexylthiophene). We test five machine learning techniques and identify random forests as the most accurate, information-dense, and easy-to-implement approach for this problem, achieving mean-absolute-error of 0.02 [× 1.6 × 10⁻¹⁹ J], R² = 0.986, predicting electronic couplings 390 times faster than quantum chemical calculations, and informing zero-field hole mobilities within 5% of prior work. We discuss strategies for identifying small effective training sets. In sum, we demonstrate an example problem where machine learning techniques provide an effective reduction in computational costs while helping to understand underlying structure–property relationships in a materials system with broad applicability.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Publication Information

Miller, Evan D.; Jones, Matthew L.; Henry, Mike M.; Stanfill, Bryan; and Jankowski, Eric. (2019). "Machine Learning Predictions Electronic Couplings for Charge Transport Calculations of P3HT". AIChE Journal, e16760-1 - e16760-10. https://doi.org/10.1002/aic.16760