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Abstract Organic solar cells (OSCs) are one of the most promising cost‐effective options for utilizing solar energy, and, while the field of OSCs has progressed rapidly in device performance in the past few years, the stability of nonfullerene OSCs has received less attention. Developing devices with both high performance and long‐term stability remains challenging, particularly if the material choice is restricted by roll‐to‐roll and benign solvent processing requirements and desirable mechanical durability. Building upon the ink (toluene:FTAZ:IT‐M) that broke the 10% benchmark when blade‐coated in air, a second donor material (PBDB‐T) is introduced to stabilize and enhance performance with power conversion efficiency over 13% while keeping toluene as the solvent. More importantly, the ternary OSCs exhibit excellent thermal stability and storage stability while retaining high ductility. The excellent performance and stability are mainly attributed to the inhibition of the crystallization of nonfullerene small‐molecular acceptors (SMAs) by introducing a stiff donor that also shows low miscibility with the nonfullerene SMA and a slightly higher highest occupied molecular orbital (HOMO) than the host polymer. The study indicates that improved stability and performance can be achieved in a synergistic way without significant embrittlement, which will accelerate the future development and application of nonfullerene OSCs.
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This content will become publicly available on July 1, 2026
Advances in the Design and Discovery of Organic Semiconductors Aided by Machine Learning
Organic semiconductors (OSCs) offer the capacity for distinctive and finely tuned electronic, optical, thermal, and mechanical properties, making them of interest across a range of energy generation and storage, sensor, lighting, display, and electronics applications. The pathway from molecular building block design to material, however, is complicated by complex synthesis– processing–structure–property–function relationships that are inherent to OSCs. The adoption of artificial intelligence (AI) tools, including the subset of AI referred to as machine learning (ML), into the materials design and discovery pipeline offers significant potential to overcome the multifaceted roadblocks along this pathway. Here, we review recent advances in the application of AI/ML for OSCs, with a focus on the development and use of ML. We present a brief primer on ML models and then highlight efforts wherein ML is used to predict molecular and material properties and discover new molecular building blocks and OSCs.
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- Award ID(s):
- 2323422
- PAR ID:
- 10635150
- Publisher / Repository:
- Annual Reviews
- Date Published:
- Journal Name:
- Annual Review of Materials Research
- Volume:
- 55
- Issue:
- 1
- ISSN:
- 1531-7331
- Page Range / eLocation ID:
- 285 to 306
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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