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Seo, Dong-Hwa
Computational Energy Materials Science Lab
Research Interests
  • 본 연구실에서는 제일원리 (first-principles) 전산모사 기법을 통해 이차전지용 전극 소재와 고체 전해질 소재에 대해 원자 단위에서 깊이 있게 이해하고 이를 바탕으로 신규 소재를 개발하고 기존 소재의 성능 향상시키는 연구를 진행하고 있습니다. 또한 인공지능 (artificial intelligence)과 기계학습 (Machine learning), 로봇공학 (robotics)을 조합하여 자동 합성/분석을 통한 재료 개발에 대한 연구를 진행하고 있습니다.

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Exploring ultrafast flow chemistry by autonomous self-optimizing platform

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Title
Exploring ultrafast flow chemistry by autonomous self-optimizing platform
Author
Ahn, Gwang-NohKang, Ji-HoLee, Hyune-JeaPark, Byung EonKwon, MinjunNa, Gi-SuKim, HeejinSeo, Dong-HwaKim, Dong-Pyo
Issue Date
2023-02
Publisher
Elsevier BV
Citation
CHEMICAL ENGINEERING JOURNAL, v.453, pp.139707
Abstract
The rapid development of novel synthetic routes for pharmaceutical compounds is highly attractive for overcoming pandemic and epidemic-prone diseases like COVID-19. Herein, we report an automated microreactor platform (AMP) with Bayesian optimization (BO) that can autonomously explore the optimal conditions for ultrafast synthesis of biologically active thioquinazolinone. First, AMP operation is successfully demonstrated with full control of quantitative variables, specifically reaction volume, temperature, and flow rate, allowing to sequentially conduct a total of 80 experiments planned by the user. Next, BO enables the AMP to autonomously self-optimize the reaction conditions, demonstrating the high efficiency of the fully automated AMP. The fully automated approach is extended to optimize more complex variables including a categorical variable (i.e. the type of organolithium for synthesis), revealing that phenyllithium (PhLi) gives superior yield for synthesizing thioquinazolinone. In addition, the autonomous AMP is utilized for combinatorial chemistry to sequentially synthesize a library composed of nine types of S-benzylic thioquinazolinone under autonomously optimized conditions within only 20 min.
URI
https://scholarworks.unist.ac.kr/handle/201301/59787
DOI
10.1016/j.cej.2022.139707
ISSN
1385-8947
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