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

Tandem organic solar cells can potentially drastically improve the power conversion efficiency over single-junction devices. However, there is limited research on device development and often 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 1225 donor/non-fullerene acceptor (NFA) pairs containing 1001 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 (GAs) 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 GAs reveal that arranging the monomers as a smallblock copolymer may be beneficial instead of the typical alternating copolymer. The GAs for selecting 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.