Designing Efficient Tandem Organic Solar Cells with Machine Learning and Genetic Algorithms

19 July 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Tandem organic solar cells can potentially drastically improve the PCE over single-junction devices. However, there is limited research on device development and often only ca. 1% improvement over single-junction devices. Because of the complex nature of organic material compatibility and properties, such as energy-level alignment and maximizing absorption spectra, and the vastness of chemical space, computational guidance is vital. The first part of this work uses a new data set of 1,225 donor/non-fullerene acceptor (NFA) pairs containing 1,001 unique pairs, one of the largest to date, to train an ensemble machine learning model to predict device efficiency (RMSE =1.60 +/- 0.14%). Next, a series of genetic algorithms (GA) are used to discover high-performing NFAs and polymer donors, and then combinations of them for potential high-efficiency tandem cells. Interesting design motifs show up in high-performing NFAs, such as diphenylamine substituents on the core and 3D terminal groups. The donor polymers from the GA reveal that it may be beneficial to arrange the monomers as a small-block copolymer instead of the common alternating copolymer. The GAs for selection of tandem cell materials successfully find material combinations, that when in a device together, have strong absorption across the entire visible-near-IR spectrum. Computational guidance is critical for the selection of tandem OSC materials, with genetic algorithms proving a highly successful technique.


organic solar cells
tandem photovoltaics
computational design
computational screening

Supplementary materials

Supporting Information
Details on performance of the machine learning models on the full dataset, common core and terminal units from the top candidates from the GA searches.

Supplementary weblinks


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