通过线性组合策略和机器学习实现自动反应优化的实时内联红外分析。

IF 6.2 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Yosuke Ashikari, Takashi Tamaki, Kyosuke Tomite, Yuya Yonekura, Aiichiro Nagaki
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引用次数: 0

摘要

自动化通过提高效率、准确性和再现性,已经彻底改变了许多领域。然而,在有机化学中,自动化反应优化和分析等关键任务仍然是一个重大挑战。为了加速有机化学研究和发展的进步,我们提出了一个基于傅立叶变换红外光谱实时在线分析的全自动化系统,并辅以神经网络模型。为了快速收集数据,使用光谱强度的线性组合作为产量预测模型的训练数据。利用该模型,我们证明了Suzuki-Miyaura交叉耦合的实时产量预测具有显著的准确性。通过将该产率预测模型与实时在线分析和流动化学设置相结合,我们开发了一个全自动系统,用于快速有效地优化反应条件和过程分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Real-time inline-IR-analysis via linear-combination strategy and machineś learning for automated reaction optimization.

Real-time inline-IR-analysis via linear-combination strategy and machineś learning for automated reaction optimization.

Real-time inline-IR-analysis via linear-combination strategy and machineś learning for automated reaction optimization.

Real-time inline-IR-analysis via linear-combination strategy and machineś learning for automated reaction optimization.

Automation has revolutionized many fields by improving efficiency, accuracy, and reproducibility. However, in organic chemistry, automating key tasks such as reaction optimization and analysis remains a significant challenge. To accelerate advancements in organic chemistry research and development, we propose a fully automated system based on real-time inline analysis performed by Fourier-transform infrared spectroscopy and assisted by a neural network model. To rapidly collect data, a linear combination of spectral intensities was used as training data for a yield prediction model. Using this model, we demonstrated real-time yield prediction of Suzuki-Miyaura cross-coupling with remarkable accuracy. By combining this yield prediction model with real-time inline analysis and a flow chemistry setup, we have developed a fully automated system for the rapid and efficient optimization of reaction conditions and process analysis.

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来源期刊
Communications Chemistry
Communications Chemistry Chemistry-General Chemistry
CiteScore
7.70
自引率
1.70%
发文量
146
审稿时长
13 weeks
期刊介绍: Communications Chemistry is an open access journal from Nature Research publishing high-quality research, reviews and commentary in all areas of the chemical sciences. Research papers published by the journal represent significant advances bringing new chemical insight to a specialized area of research. We also aim to provide a community forum for issues of importance to all chemists, regardless of sub-discipline.
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