posted on 2023-03-28, 19:12authored byBrianna
L. Greenstein, Geoffrey R. Hutchison
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 small-block 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.