Deep Transfer Learning: A Fast and Accurate Tool to Predict the Energy Levels of Donor Molecules for Organic Photovoltaics

Abstract

Abstract Molecular engineering is driving the recent efficiency leaps in organic photovoltaics (OPVs). A presynthetic determination of frontier energy levels makes the screening of potential molecules more efficient, exhaustive, and cost‐effective. Here, a convolutional neural network is developed to predict the highest occupied and lowest unoccupied molecular orbital (HOMO/LUMO) levels of donor molecules for OPV. The model takes a 2D structure image and returns a prediction of its HOMO/LUMO levels comparable to experimental values. Insufficient experimental datasets are overcome with transfer learning where the model is initially trained on the large Harvard Clean Energy Project dataset and then fine‐tuned using experimental data from the Harvard Organic Photovoltaic dataset. Error margins on predicted HOMO/LUMO levels below 200 meV are achieved, without any chemical knowledge implemented. Noticeably, the model outputs have higher accuracy and precision than corresponding density functional theory (DFT) estimations. The model and its limitations are further tested on a home‐built dataset of commercially available donor polymers reported in OPVs (e.g., P3HT, PTB7‐Th, PM6, D18). The results demonstrate both the practical utility of this model, to foster rational molecular engineering for OPV optimization, and the potential for deep learning techniques, in general, to revolutionize the energy materials research and development sector.