Microfluidic Screening‐Assisted Machine Learning to Investigate Vertical Phase Separation of Small Molecule:Polymer Blend
2021 · Advanced Materials
저자
Jeong‐Chan Lee, Jun Hee Woo, Hojun Lee, Minho Lee, Heejin Woo, Seunghyeok Baek, 남재욱 (Jaewook Nam), Joo Yong Sim, Steve Park
연구주제
Machine Learning in Materials Science · Organic Electronics and Photovoltaics · Nanomaterials and Printing Technologies
NTIS 과학기술표준분류
배정 근거: AI 키워드가 잡은 NTIS 과제의 분류(정책 연계 맵과 같은 방법).
AI 연구 키워드·방법
microfluidic screening 유기 박막트랜지스터(OTFT) small molecule polymer blend vertical phase separation
키워드 (OpenAlex)
vertical phase separation microfluidic screening solution-processed thin films organic transistors ink composition Bayesian optimization Gaussian process regression parameter optimization
초록
Solution-based thin-film processing is a widely utilized technique for the fabrication of various devices. In particular, the tunability of the ink composition and coating condition allows precise control of thin-film properties and device performance. Despite the advantage of having such tunability, the sheer number of possible combinations of experimental parameters render it infeasible to efficiently optimize device performance and analyze the correlation between experimental parameters and device performance. In this work, a microfluidic screening-embedded thin-film processing technique is developed, through which thin-films of varying ratios of small molecule semiconductor:polymer blend are simultaneously generated and screened in a time- and resource-efficient manner. Moreover, utilizing the thin-films of varying combinations of experimental parameters, machine learning models are …
이 논문을 지원한 국가R&D 과제 (NTIS 성과)
| 연도 | 과제 | 부처 | 사업 | 수행기관 | NTIS 분류(1순위) |
|---|---|---|---|---|---|
| 2022 | 화공분야연구인력양성사업단 | 교육부 | 4단계두뇌한국21사업(R&D) | 서울대학 | – |
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연구주제 1
Machine Learning in Materials Science