Automated Flow Synthesis: In-Line Reaction Monitoring and Machine Learning for the Optimisation of Continuous Flow Photocatalytic Reactions

自动流动合成：用于优化连续流动光催化反应的在线反应监测和机器学习

• 批准号: 2894726
• 金额: --
• 依托单位: Heriot-Watt University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2894726
• 关键词: Automated; Flow; Synthesis; Line; Reaction

This is a PhD research project in Chemistry. We propose a new and ambitious project to utilise in-line benchtop NMR and/or UV-vis monitoring of continuous flow photocatalytic radical decarboxylation reactions, exploiting real time data analytics in a digital feedback-loop to optimise several reaction parameters. Photocatalytic direct C-H Minisci functionalisations of heterocycles will be developed and optimised in this way, as these scaffolds are highly relevant to drug discovery programmes. Our system will enable technology for more efficient photocatalytic reactions including scale up processes compared to batch reactions. The project aims to provide proof-of-principle towards an ultimate goal of automated development and optimisation, using control theory and machine learning techniques, of continuous flow photocatalytic reactions. Stepwise objectives are therefore: 1. Optimise in continuous flow the photochemical Minisci transformations 2. Integrate in-line/on-line monitoring systems (NMR and UV-Vis) with the flow system as a unified platform for the generation of training data 3. Model the input/output processes of the continuous flow and design control/reinforcement learning algorithms to maximise meaningful metrics (e.g., selectivities, conversions and yields)

这是一个化学博士研究项目。我们提出了一个新的雄心勃勃的项目，利用在线台式NMR和/或UV-vis监测连续流动光催化自由基脱羧反应，利用数字反馈回路中的实时数据分析来优化几个反应参数。光催化直接C-H - Minisci杂环功能化将以这种方式开发和优化，因为这些支架与药物发现计划高度相关。我们的系统将使技术更有效的光催化反应，包括与批量反应相比的规模过程。该项目旨在利用控制理论和机器学习技术，为连续流动光催化反应的自动化开发和优化的最终目标提供原理证明。因此，分步目标是：优化在连续流光化学迷你转换2。将在线/在线监测系统（NMR和UV-Vis）与流系统集成为生成训练数据的统一平台。为连续流的输入/输出过程建模，并设计控制/强化学习算法，以最大限度地提高有意义的指标（例如，选择性、转化率和产量）。