Machine learning property prediction for organic photovoltaic devices

Nastaran Meftahi, Mykhailo Klymenko, Andrew J. Christofferson, Udo Bach, David A. Winkler, Salvy P. Russo

Research output: Contribution to journalArticleResearchpeer-review


Organic photovoltaic (OPV) materials are promising candidates for cheap, printable solar cells. However, there are a very large number of potential donors and acceptors, making selection of the best materials difficult. Here, we show that machine-learning approaches can leverage computationally expensive DFT calculations to estimate important OPV materials properties quickly and accurately. We generate quantitative relationships between simple and interpretable chemical signature and one-hot descriptors and OPV power conversion efficiency (PCE), open circuit potential (Voc), short circuit density (Jsc), highest occupied molecular orbital (HOMO) energy, lowest unoccupied molecular orbital (LUMO) energy, and the HOMO–LUMO gap. The most robust and predictive models could predict PCE (computed by DFT) with a standard error of ±0.5 for percentage PCE for both the training and test set. This model is useful for pre-screening potential donor and acceptor materials for OPV applications, accelerating design of these devices for green energy applications.

Original languageEnglish
Article number166
Number of pages8
Journalnpj Computational Materials
Issue number1
Publication statusPublished - 1 Dec 2020

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