From Molecules to Devices: A Multiscale Approach to Evaluating Organic Photovoltaics

Due to their efficient molecular
design, nonfullerene acceptors
(NFAs) have significantly advanced organic photovoltaics (OPVs). However,
the lack of models to screen and evaluate candidate NFAs based on
the resulting device performance has impeded the rapid development
of high-performance molecules. This work introduces a computational
framework utilizing a kinetic Monte Carlo (kMC) model to derive device
parameters from molecular properties computed through first principles.
By analyzing the quantum chemical properties of diverse dimeric conformers,
we estimate the relative probabilities of microscopic processes such
as charge separation, recombination, and transport along with charge
transfer state formation in the active layer of OPVs. These probabilities
set up a random walk of charge carriers in a grid with disordered
molecular sites, allowing us to track their average behavior and calculate
key device parameters. Our model consistently predicts measured device
parameters, including the short-circuit current and open-circuit voltage,
for OPVs with diverse NFAs with high accuracy. Additionally, we applied
the model to evaluate donor–acceptor combinations of known
compounds and newly designed NFA molecules, identifying high-performing
pairs. This model offers a computationally efficient approach for
designing novel NFA molecules and optimizing the OPV performance.