Computationally-Guided and Synthetic Realisation of Novel Catalysts for Cross-Coupling Reactions

交叉偶联反应新型催化剂的计算引导和合成实现

• 批准号: 2645227
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2645227
• 关键词: Computationally; Guided; Synthetic; Realisation; Novel

In this project, property descriptors of new ligand designs will be calculated computationally. In parallel, descriptors for known ligands/catalysts will be combined with experimental screening data and will then be analysed using Principal Component Analysis (PCA), multivariate-regression and machine learning (ML), leading to mechanistic hypotheses and predictions about the properties of the catalysts. Promising catalysts will then be synthesised and tested in the lab, while the mechanism of reaction will also be studied computationally, with a view to developing additional descriptors to capture catalyst-substrate matching. A combination of design of experiments (DoE) and high throughput screening will be used to maximise the efficiency of this process. Experimental results will then be fed back into the initial models to improve the design of the ligands. This will be an iterative process, giving insight into important properties for catalyst design and catalyst-substrate matching.

在这个项目中，新的配体设计的性质描述符将通过计算来计算。同时，已知配体/催化剂的描述符将与实验筛选数据相结合，然后将使用主成分分析(PCA)、多元回归和机器学习(ML)进行分析，从而产生关于催化剂性质的机械性假设和预测。然后将合成有希望的催化剂并在实验室中进行测试，同时还将通过计算研究反应机理，以期开发更多的描述符来捕捉催化剂与底物的匹配。实验设计(DOE)和高通量筛选的结合将被用来最大限度地提高这一过程的效率。然后将实验结果反馈到初始模型中，以改进配体的设计。这将是一个迭代的过程，让我们深入了解催化剂设计和催化剂-底物匹配的重要性质。