Machine learning (ML) is a field of computer science that uses algorithms and techniques for
automating solutions to complex problems that are hard to program using conventional …

With the emergence of ADA'DA‐type (Y‐series) non‐fullerene acceptors (NFAs), the power
conversion efficiencies (PCEs) of organic photovoltaic devices have been constantly …

Side‐chain tailoring is a promising method to optimize the performance of organic solar cells
(OSCs). However, asymmetric alkyl chain‐based small molecular acceptors (SMAs) are still …

Organic solar cells are the most promising candidates for future commercialization. This goal
can be quickly achieved by designing new materials and predicting their performance …

Graded bulk-heterojunction (G-BHJ) with well-defined vertical phase separation has
potential to surpass classical BHJ in organic solar cells (OSCs). In this work, an effective G …

This paper provides a comprehensive overview of organic photovoltaic (OPV) cells,
including their materials, technologies, and performance. In this context, the historical …

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 …

All-polymer solar cells (all-PSCs) consisting of polymer donors (P Ds) and polymer
acceptors (P As) have drawn tremendous research interest in recent years. It is due to not …

Herein, we synthesized new hetero‐halogenated end groups with well‐determined
fluorinated and chlorinated substitutions (o‐FCl‐IC and FClF‐IC), and synthesized …

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 …