Project Details
Projekt
Exploring tailored Ru-triphos catalysts for hydrogenation reactions by combination of experimental, computational, and machine learning techniques
Subject Area
Theoretical Chemistry: Electronic Structure, Dynamics, Simulation
Term
since 2022
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 497198902
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.
DFG Programme
Priority Programmes
