Various industries rely upon transition metal complexes to efficiently catalyze chemical reactions. These transition metal complexes often consist of precious metals, which are scarce and expensive. Therefore, a shift towards catalysts containing earth-abundant metals is necessary. Computational catalyst screening has the potential to accelerate this shift by reducing catalyst discovery time. The first step of computational catalyst screening consists of obtaining a digital representation of the catalyst. Usually, a fixed a priori ligand configuration of TM-complexes is assumed to represent the catalyst. However, this approach of catalyst representation might not capture the influence of alternative ligand configurations on the observed catalytic behavior. In the context of high-throughput in-silico catalyst screening, this study aims to evaluate the influence of ligand configurations on the stability and physicochemical properties of transition metal complexes. An automated workflow for the generation of complexes, complex sorting based on ligand configuration, DFT geometry optimization, and descriptor extraction is employed. Ensembles of ligand configurations are generated for iridium(III), ruthenium(II), and manganese(I) complexes featuring 88 bisphosphine bidentate ligands. Based on DFT-optimized geometries, analysis reveals a preference for a specific ligand configuration for iridium(III) complexes. However, this preference is not observed for ruthenium(II) and manganese(I) complexes. Furthermore, for the majority of ruthenium(II) and manganese(I) complexes, multiple ligand configurations are found within a 10 kJ/mol range from the most favorable one. The analysis of thermodynamic, electronic, geometric, and steric descriptors reveals that none of the descriptors consistently correlates to the preferred ligand configuration. These findings indicate that a priori selection of ligand configuration may result in insufficient coverage and representation of key catalyst features for predictive in-silico chemical space exploration.

Manual data processing is a tedious task that should be automated. Besides saving time, automated data processing also fights other problems in chemistry. Automated data processing in a normalized way makes data analysis between different experiments possible and can remove biases. In this study, kinetic data workflow is studied and a python script for automated data processing is made. Different experiments on the hydrogenation reaction of ethyl hexanoate using a ruthenium-PNN catalyst have been performed to obtain kinetic hydrogenation data. Reaction temperature was set to either 50, 70 or 110 °C and catalyst loading was either 50, 100 or 200 ppm. In total, nine experiments were performed. Gas chromatography and pressure readings during the reaction are used for analysis. Analysis is done on two different machine that report data in a different template and format. Therefore, a python script was written to automatically process the raw data obtained by these analysis methods. The written script imports obtained data, calculates new normalized parameters, concentration of different species for example, creates different plots of processed data and exports an excel file containing normalized data. This exported excel file was used to further examine catalyst kinetics. It was found for example, that at 50 and 70 °C, the reaction order of the catalyst is below one and at 90 and 110 °C catalyst reaction order is above one. The made processing script has improved data workflow, while data processing time has been reduced.