Autonomous Optimization of Discrete Reaction Parameters: Mono-Functionalization of a Bifunctional Substrate via a Suzuki-Miyaura Reaction

ChemRxiv - Organic Chemistry 

The systematic, autonomous optimization of discrete reaction parameters in organic synthesis remains unexplored, offering the potential to revolutionize synthetic methodology and expand the scope and efficiency of reaction development. Here, we introduce a fully integrated system that combines an automated synthesis robot, supercritical fluid chromatography, and Bayesian optimization to achieve the selective mono-functionalization of a bifunctional substrate via a Suzuki–Miyaura reaction. By leveraging variations in the ligand, base, and solvent, our approach enables the precise tuning of the reaction parameters to achieve the targeted selectivity. Preliminary trials spanning 68 conditions identified eight critical descriptors, providing a framework for the systematic characterization of the parameter. This framework supports autonomous experimentation across 192 reaction conditions, comprising the initial four conditions and 47 iterative cycles, which ultimately enhanced the product yield from initially 9.5% to 49%. The generated mono-functionalized products serve as promising building blocks for organic photoelectronic applications, highlighting the far-reaching impact of autonomous, data-driven methodologies in synthetic and materials chemistry.

Seiji Akiyama,1,2 Hayaka Akitsu,1,2 Ryo Tamura,3,4,5 Wataru Matsuoka,1,2,6 Satoshi Maeda,1,2,6 Koji Tsuda,3,4,5 and Yuuya Nagata,1,2

1 Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Hokkaido 001-0021, Japan

2 JST, ERATO Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project, Sapporo, Hokkaido 060-0810, Japan

3 Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba, 277-8561, Japan

4 Center for Basic Research on Materials, National Institute for Materials Science (NIMS), 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan

5 RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan

6 Department of Chemistry, Faculty of Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan

DOI: https://doi.org/10.26434/chemrxiv-2024-bnj6p-v2

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