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AI-driven robotic chemist for autonomous synthesis of organic molecules

Taesin Ha, Dongseon Lee, Youngchun Kwon, Min Sik Park, Lee Sangyoon, Jaejun Jang, B.‐D. Choi, Hyunjeong Jeon, Jeonghun Kim, Hyundo Choi, Hyung-Tae Seo, Wonje Choi, Wooram Hong, Young Jin Park, Junwon Jang, Joon-Kee Cho, Bosung Kim, Hyukju Kwon, Won Seok Oh, Jin Woo Kim

Year
2023
Citations
91

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.

Keywords

ExpeditingAutomationComputer scienceLaboratory automationRobotArtificial intelligenceChemistBiochemical engineeringSystems engineeringEngineering

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