| 학회 |
한국고분자학회 |
| 학술대회 |
2021년 가을 (10/20 ~ 10/22, 경주컨벤션센터) |
| 권호 |
46권 2호 |
| 발표분야 |
미래소재: 태양광 에너지 활용 소재 기술 |
| 제목 |
Machine learning-assisted development of organic photovoltaics via high-throughput in situ formulation |
| 초록 |
Discovery of high-performance non-fullerene acceptors and ternary blend systems have resulted in a breakthrough in the efficiency of organic photovoltaics (OPVs) and created new opportunities for commercialization. Here we show a new research approach to develop OPVs via industrial roll-to-roll (R2R) slot die coating in conjunction with in-situ formulation technique and machine learning (ML) technology. The formulated PM6:Y6:IT-4F ternary blends deposited on continuously moving substrates resulted in high-throughput fabrication of OPVs with various compositions. The system was used to produce training data for ML prediction. Composition/deposition parameters, referred to as deposition densities, and efficiencies of 2218 devices were used to screen ML algorithms and to train an ML model based on Random Forest regression algorithm. Generated model was used to predict high-performance formulations and the prediction was experimentally validated by fabricating 10.2% efficiency devices. |
| 저자 |
김진영, 안나경
|
| 소속 |
울산과학기술원 |
| 키워드 |
organic photovoltaics; machine learning; high-throughput; roll-to-roll; deposition density
|
| E-Mail |
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