Abstract Machine learning is a powerful tool that can provide a way to revolutionize the
material science. Its use for the designing and screening of materials for polymer solar cells …

Promoted understanding of nanostructures and their functions significantly rely on rapid
progress of nanotechnology within a few decades. It would be a fruitful way to consider …

The power conversion efficiency (PCE) of organic solar cells (OSCs) is increasing
continuously, however, commercialization is far from being achieved due to the very high …

Nonfullerene, a small molecular electron acceptor, has substantially improved the power
conversion efficiency of organic photovoltaics (OPVs). However, the large structural freedom …

Non-fullerene acceptors (NFAs) are excellent light harvesters, yet the origin of their high
optical extinction is not well understood. In this work, we investigate the absorption strength …

In contrast to the inorganic and perovskite solar cells, organic photovoltaics (OPV) depend
on a series of charge generation and recombination processes, which complicates …

Despite the progress in machine learning (ML) in terms of prediction of power conversion
efficiency (PCE) in organic photovoltaics (OPV), the effectiveness of ML in practical …

This study presents the synthesis of small-molecule acceptors having the structure A–D–A′–
D–A—where A, A′, and D represent the end group, the core and π-bridge unit, respectively …

Organic solar cells (OSCs) hold great potential as a photovoltaic technology for practical
applications. However, the traditional experimental trial-and-error method for designing and …

Tandem organic solar cells can potentially drastically improve the power conversion
efficiency over single-junction devices. However, there is limited research on device …