Journal
NPJ COMPUTATIONAL MATERIALS
Volume 6, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41524-020-00429-w
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Funding
- Australian Government through Australian Research Council (ARC) under Centre of Excellence scheme [CE170100026]
- Pawsey Supercomputer Centre.
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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 (V-oc), short circuit density (J(sc)), 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.
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