Publikationen

Transition metal complexes have played crucial roles in various homogeneous catalytic processes due to their exceptional versatility. This adaptability stems not only from the central metal ions but also from the vast array of choices of the ligand spheres, which form an enormously large chemical space. For example, Rh complexes, with a well-designed ligand sphere, are known to be efficient in catalyzing the C-H activation process in alkanes. To investigate the structure-property relation of the Rh complex and identify the optimal ligand that minimizes the calculated reaction energy ΔE of an alkane C-H activation, we have applied a Δ-machine learning method trained on various features to study 1743 pairs of reactants (Rh(PLP)(Cl)(CO)) and intermediates (Rh(PLP)(Cl)(CO)(H)(propyl)). Our findings demonstrate that the models exhibit robust predictive performance when trained on features derived from electron density (R2 = 0.816), and SOAPs (R2 = 0.819), a set of position-based descriptors. Leveraging the model trained on xTB-SOAPs that only depend on the xTB-equilibrium structures, we propose an efficient and accurate screening procedure to explore the extensive chemical space of bisphosphine ligands. By applying this screening procedure, we identify ten newly selected reactant-intermediate pairs with an average ΔE of 33.2 kJ mol−1, remarkably lower than the average ΔE of the original data set of 68.0 kJ mol−1. This underscores the efficacy of our screening procedure in pinpointing structures with significantly lower energy levels.

The activation step of Vaska-type Rh(I) complexes, such as the photocleavage of the Rh‑CO bond, plays an important role in the subsequent C-H activation. To elucidate the details of the photochemistry of Vaska-type Rh(I) complexes, such as trans-Rh(PMe3)2(CO)(Cl), we here present a computationally derived picture as obtained at the density functional level of theory (DFT) in combination with multireference wavefunction-based methods. We have identified that the photocleavage of CO proceeds via the metal-centered excited state, which is populated through intersystem crossing (ISC) from the dipole-allowed excited state S1. Moreover, the present study unraveled the reasons for the low C-H activation efficiency when using Rh featuring the bidentate ligand 1,2-bis(dimethylphosphino)ethane (dmpe), namely due to its unfavorable photochemical properties, i.e., the small driving force for light-induced CO loss and the fast deactivation of 3MC state back to the singlet ground state. In this study, we provide theoretical insight into mechanistic details underlying the light-induced CO dissociation process, for Rh complexes featuring PMe3 and dmpe ligands.

This publication introduces a novel open-access 31P Nuclear Magnetic Resonance (NMR) shift database. With 14,250 entries encompassing 13,730 distinct molecules from 3,648 references, this database offers a comprehensive repository of organic and inorganic compounds. Emphasizing single-phosphorus atom compounds, the database facilitates data mining and machine learning endeavors, particularly in signal prediction and Computer-Assisted Structure Elucidation (CASE) systems. Additionally, the article compares different models for 31P NMR shift prediction, showcasing the database’s potential utility. Hierarchically Ordered Spherical Environment (HOSE) code-based models and Graph Neural Networks (GNNs) perform exceptionally well with a mean squared error of 11.9 and 11.4 ppm respectively, achieving accuracy comparable to quantum chemical calculations.

Dialysis diffusion kinetics are investigated via in situ NMR spectroscopy for numerous different raw polymeric solutions to result in a general guideline for polymer purification using dialysis. In several approaches, a polymer was on purpose contaminated with its respective monomer, regenerated conducting conventional dialysis and monitored online utilizing in situ NMR spectroscopy. Consequently, polymer type and molar mass, monomer type, molar mass cut-off of the dialysis tubing and type of solvent were varied resulting in 29 different purification approaches and over 40 000 NMR-spectra. As a result, several major parameters were identified affecting the purification process significantly such as the chosen solvent, viscosity and alpha value. On the contrary, parameters such as dialysis tubing molar mass cut-off and molar mass of the polymer did not affect the purification in a significant manner. Furthermore, physical properties such as density, viscosity, alpha value, and dipole moment of the ingredients were combined in a principal component analysis in order to identify the most important parameters.

The C-H activation and subsequent carbonylation mediated by metal complexes, i. e., Rh(I) complexes, has drawn considerable attention in the past. To extend the mechanistic insight from Rh complexes featuring monodentate ligands like P(Me)3 towards more active bisphosphines (PLP), a computationally derived fully conclusive mechanistic picture of the Rh(I)-catalyzed C-H activation and carbonylation is presented here. Depending on the nature of the bisphosphine ligand, the highest lying transition state (TS) is associated either to the initial C-H activation in [Rh(PLP)(CO)(Cl)] or to the rearrangement of the chloride in [Rh(PLP)(H)(R)(Cl)]. The chloride rearrangement was found to play a key role in the subsequent carbonylation. A set of 20 complexes of different architectures was studied, in order to fine tune the C-H activation in a knowledge-driven approach. The computational analysis suggests that a flexible ligand architecture with aromatic rings can potentially increase the performance of Rh-based catalysts for the C-H activation.