AI-driven robotic chemist for autonomous synthesis of organic moleculesopen access
- Authors
- Ha, Taesin; Lee, Dongseon; Kwon, Youngchun; Park, Min Sik; Lee, Sangyoon; Jang, Jaejun; Choi, Byungkwon; Jeon, Hyunjeong; Kim, Jeonghun; Choi, Hyundo; Seo, Hyung-Tae; Choi, Wonje; Hong, Wooram; Park, Young Jin; Jang, Junwon; Cho, Joonkee; Kim, Bosung; Kwon, Hyukju; Kim, Gahee; Oh, Won Seok; Kim, Jin Woo; Choi, Joonhyuk; Min, Minsik; Jeon, Aram; Jung, Yongsik; Kim, Eunji; Lee, Hyosug; Choi, Youn-Suk
- Issue Date
- Nov-2023
- Publisher
- AMER ASSOC ADVANCEMENT SCIENCE
- Citation
- SCIENCE ADVANCES, v.9, no.44
- Indexed
- SCIE
SCOPUS
- Journal Title
- SCIENCE ADVANCES
- Volume
- 9
- Number
- 44
- URI
- https://scholarworks.gnu.ac.kr/handle/sw.gnu/68611
- DOI
- 10.1126/sciadv.adj0461
- ISSN
- 2375-2548
2375-2548
- Abstract
- The automation of organic compound synthesis is pivotal for expediting the development of such compounds. In addition, enhancing development efficiency can be achieved by incorporating autonomous functions alongside automation. To achieve this, we developed an autonomous synthesis robot that harnesses the power of artificial intelligence (AI) and robotic technology to establish optimal synthetic recipes. Given a target molecule, our AI initially plans synthetic pathways and defines reaction conditions. It then iteratively refines these plans using feedback from the experimental robot, gradually optimizing the recipe. The system performance was validated by successfully determining synthetic recipes for three organic compounds, yielding that conversion rates that outperform existing references. Notably, this autonomous system is designed around batch reactors, making it accessible and valuable to chemists in standard laboratory settings, thereby streamlining research endeavors.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.