Identifying structure-absorption relationships and predicting absorption strength of non-fullerene acceptors for organic photovoltaics
Non-fullerene acceptors (NFAs) are excellent light harvesters, yet the origin of such high optical extinction is not well understood. In this work, we investigate the absorption strength of NFAs by building a database of time-dependent density functional theory (TDDFT) calculations of ~500 pi-conjug...
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Zusammenfassung: | Non-fullerene acceptors (NFAs) are excellent light harvesters, yet the origin
of such high optical extinction is not well understood. In this work, we
investigate the absorption strength of NFAs by building a database of
time-dependent density functional theory (TDDFT) calculations of ~500
pi-conjugated molecules. The calculations are first validated by comparison
with experimental measurements on liquid and solid state using common fullerene
and non-fullerene acceptors. We find that the molar extinction coefficient
({\epsilon}_(d,max)) shows reasonable agreement between calculation in vacuum
and experiment for molecules in solution, highlighting the effectiveness of
TDDFT for predicting optical properties of organic pi-conjugated molecules. We
then perform a statistical analysis based on molecular descriptors to identify
which features are important in defining the absorption strength. This allows
us to identify structural features that are correlated with high absorption
strength in NFAs and could be used to guide molecular design: highly absorbing
NFAs should possess a planar, linear, and fully conjugated molecular backbone
with highly polarisable heteroatoms. We then exploit a random decision forest
to draw predictions for {\epsilon}_(d,max) using a computational framework
based on extended tight-binding Hamiltonians, which shows reasonable predicting
accuracy with lower computational cost than TDDFT. This work provides a general
understanding of the relationship between molecular structure and absorption
strength in pi-conjugated organic molecules, including NFAs, while introducing
predictive machine-learning models of low computational cost. |
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DOI: | 10.48550/arxiv.2203.09990 |