Engineering Commons^™

Machine Learning Predictions Electronic Couplings For Charge Transport Calculations Of P3ht, Evan D. Miller, Matthew L. Jones, Mike M. Henry, Bryan Stanfill, Eric Jankowski

Materials Science and Engineering Faculty Publications and Presentations

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, …