Turning Data into Knowledge - Data-Led Catalyst Optimisation

有机金属催化剂的使用在工业上已经得到了广泛的应用，这在很大程度上是由于它们的高选择性和催化活性。然而，当应用于不同的底物时，过渡金属催化反应的机理往往会发生变化，从而导致收率和选择性的降低。通常，当使用新的底物时，催化剂优化是必要的，这可能是一个时间和资源消耗的努力。因此，人们越来越倾向于从寻找一种特殊的催化剂来普遍应用于单个催化过程，而转向预测给定底物的最佳条件和催化剂设计的能力。为此，布里斯托尔的研究人员开发了一系列配体知识库（LKBs）；有机金属催化配体数据库，以及描述每个配体的空间和电子性质的计算计算描述符。利用主成分分析（PCA），可以根据配体结构性质的相似性对其进行分组，并对其催化性能进行预测。与拜耳合作，来自整个化学领域的配体将被筛选其对各种底物的催化能力。这种高通量筛选方法将提供有关配体空间的哪些部分与给定应用的最佳性能相关的信息，以及这些部分如何作为底物的因素而变化。使用主成分分析，可以从描述符中识别出给定过程中有效催化剂的特征，并将其用作新配体的设计原则。然后将合成新的配体，以判断这种方法在催化剂发现优化中的可行性。此外，还将在LKB中添加新的配体，并对有效配体的催化机理进行研究。混合配体体系的潜在用途也将被研究作为一种方法，以进一步提高催化剂的优化过程。

The use of organometallic catalysts is well established in industry, due in large part to their high selectivity and catalytic activity. However, the mechanism of transition metal catalytic reactions is often subject to change when applied to different substrates, which can lead to decreases in yield and selectivity. Often, catalyst optimisation will be necessary when using new substrates, which can be a time and resource consuming endeavour. As such, there is a growing drive to shift away from the search for a privileged catalyst to apply ubiquitously to an individual catalytic process, and instead towards the ability to predict the optimal conditions and catalyst design for a given set of substrates. To this end, researches in Bristol have developed a range of Ligand Knowledge Bases (LKBs); databases of ligands for organometallic catalysis, and computationally calculated descriptors that describe the steric and electronic properties of each ligand. Using Principle Component Analysis (PCA), ligands can be grouped based on similarities in their structural properties, and predictions on their catalytic properties can be made. In collaboration with Bayer, ligands from across the chemical space will be screened for their catalytic capabilities towards a variety of substrates. This high-throughput screening approach will give information on what sections of the ligand space correlated with the best performance for a given application, and how those change as a factor of substrate. Using PCA, the features of effective catalysts for a given process can be identified from their descriptors and used as design principles for new ligands. New ligands will then be synthesised to judge the viability of this approach in the optimisation of catalyst discovery. In addition, any new ligands will be added to the LKB, and the catalytic mechanisms of effective ligands will be investigated. The potential use of mixed ligand systems will also be investigated as a method to further enhance the process of catalyst optimisation.