基于通过电荷约束的贝叶斯优化用于流动微反应器中电化学还原羧化反应的多参数筛选
Bayesian optimization with constraint on passed charge for multiparameter screening of electrochemical reductive carboxylation in a flow microreactor.
作者信息
Naito Yuki, Kondo Masaru, Nakamura Yuto, Shida Naoki, Ishikawa Kazunori, Washio Takashi, Takizawa Shinobu, Atobe Mahito
机构信息
Graduate School of Science and Engineering, Yokohama National University, Yokohama, Kanagawa 240-8501, Japan.
Department of Materials Science and Engineering, Graduate School of Science and Engineering, Ibaraki University, Hitachi 316-8511, Ibaraki, Japan.
出版信息
Chem Commun (Camb). 2022 Mar 22;58(24):3893-3896. doi: 10.1039/d2cc00124a.
Multiparameter screening of reductive carboxylation in an electrochemical flow microreactor was performed using a Bayesian optimization (BO) strategy. The developed algorithm features a constraint on passed charge for the electrochemical reaction, which led to suitable conditions being instantaneously found for the desired reaction. Analysis of the BO-suggested conditions underscored the physicochemical validity.
采用贝叶斯优化（BO）策略，在电化学流动微反应器中对还原羧化反应进行多参数筛选。所开发的算法对电化学反应的通过电荷量进行了约束，从而能够即时找到所需反应的合适条件。对BO建议条件的分析突出了其物理化学有效性。
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