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

在这个项目中，新的配体设计的性质描述符将计算计算。与此同时，已知配体/催化剂的描述符将与实验筛选数据相结合，然后使用主成分分析（PCA）、多元回归和机器学习（ML）进行分析，从而得出关于催化剂性质的机制假设和预测。有前景的催化剂将在实验室中进行合成和测试，同时反应的机制也将进行计算研究，以期开发更多的描述符来捕获催化剂-底物匹配。实验设计（DoE）和高通量筛选的结合将用于最大限度地提高该过程的效率。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，为催化剂设计和催化剂-底物匹配提供重要的特性。

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.