Exploring tailored Ru-triphos catalysts for hydrogenation reactions by combination of experimental, computational, and machine learning techniques

通过实验、计算和机器学习技术的结合，探索用于加氢反应的定制 Ru-triphos 催化剂

• 批准号: 497198902
• 负责人: Professor Dr. Christoph Bannwarth
• 金额: --
• 依托单位: Institut für Technische Chemie und Makromolekulare Chemie (ITMC)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/497198902?language=en
• 关键词: Exploring; tailored; Ru; triphos; catalysts

The successful homogeneously catalyzed conversion of CO2 to methanol, formic acid and formaldehyde derivatives could already be demonstrated with [Ru (E-triphosA3) (tmm)] complexes. Only a few studies have taken into account non-symmetrical ligand substitution patterns that lead to metal complexes of the type [Ru(E-triphosAB2)(tmm)] or [Ru( E-TriphosABC)(tmm)]. Due to the expansion of the chemical space caused by symmetry breaking, including the introduction of diastereomeric complexes, an investigation using experimental methods quickly becomes infeasible. In this project, the use of modern statistical and computational models, together with experimental evidence, is used to construct a scaffold that captures the reactivity of such symmetry-broken [Ru (E-triphos AB2) (tmm)] complexes and thus a reactivity prediction for the symmetry-broken complexes enables. This is achieved through the use of machine learning methods based on descriptors from cost-efficient semi-empirical electronic structure theory calculations. Together with the generated reference data of symmetrical and non-symmetrical complexes, a quick screening of the chemical space should be made possible. In order to couple the computational methods with the experimental data, detailed investigations of [Ru (E-triphos AB2) (tmm)] complexes in the hydrogenation of CO2 and levulinic acid are carried out. Thus, an extensive data set is created. Based on this set, a machine-learned structure-activity relationship model is developed with the aim of predicting the reactivity of previously unknown complexes on a substrate-specific basis.

已经可以用[Ru（E-trimethylA 3）（tmm）]络合物证明成功地将CO2均相催化转化为甲醇、甲酸和甲醛衍生物。只有少数研究考虑了非对称配体取代模式，其导致[Ru（E-trimethylAB 2）（tmm）]或[Ru（E-trimethylABC）（tmm）]类型的金属络合物。由于对称性破缺导致化学空间的扩展，包括引入非对映体络合物，使用实验方法的研究很快变得不可行。在该项目中，使用现代统计和计算模型，以及实验证据，用于构建一个支架，该支架捕获这种破碎的[Ru（E-triphos AB 2）（tmm）]络合物的反应性，从而实现对破碎络合物的反应性预测。这是通过使用机器学习方法来实现的，该方法基于来自具有成本效益的半经验电子结构理论计算的描述符。与生成的对称和非对称复合物的参考数据一起，可以快速筛选化学空间。为了将计算方法与实验数据相结合，详细研究了[Ru（E-triphos AB 2）（tmm）]配合物在CO2和乙酰丙酸加氢反应中的催化作用.因此，创建了一个广泛的数据集。在此基础上，开发了一个机器学习的结构-活性关系模型，其目的是在特定底物的基础上预测以前未知的复合物的反应性。