Rx-COSMO-CAMD: Computer-Aided Molecular Design of Reaction Solvents Based on Predictive Kinetics from Quantum Chemistry

Reaction Mechanisms and Rate Constants of Auto-Catalytic Urethane Formation and Cleavage Reactions

The chemistry of urethanes plays a key role in important industrial processes. Although catalysts are often used, the study of the reactions without added catalysts provides the basis for a deeper understanding. For the non-catalytic urethane formation and cleavage reactions, the dominating reaction mechanism has long been debated. To our knowledge, the reaction kinetics have not been predicted quantitatively so far. Therefore, we report a new computational study of urethane formation and cleavage reactions. To analyze various potential reaction mechanisms and to predict the reaction rate constants quantum chemistry and transition state theory were employed. For validation, experimental data from literature and from own experiments were used. Quantitative agreement of experiments and predictions could be demonstrated. The calculations confirm earlier assumptions that urethane formation reactions proceed via mechanisms where alcohol molecules act as auto-catalysts. Our results show that it is essential to consider several transition states corresponding to different reaction orders to enable agreement with experimental observations. Urethane cleavage seems to be catalyzed by an isourethane, leading to an observed 2nd-order dependence of the reaction rate on the urethane concentration. The results of our study support a deeper understanding of the reactions as well as a better description of reaction kinetics and will therefore help in catalyst development and process optimization.

Machine learning approaches to understand and predict rate constants for organic processes in mixtures containing ionic liquids

The ability to tailor the constituent ions in ionic liquids (ILs) is highly advantageous as it provides access to solvents with a range of physicochemical properties. However, this benefit also leads to large compositional spaces that need to be explored to optimise systems, often involving time consuming experimental work. The use of machine learning methods is an effective way to gain insight based on existing data, to develop structure-property relationships and to allow the prediction of ionic liquid properties. Here we have applied machine learning models to experimentally determined rate constants of a representative organic process (the reaction of pyridine with benzyl bromide) in IL-acetonitrile mixtures. Multiple linear regression (MLREM) and artificial neural networks (BRANNLP) were both able to model the data well. The MLREM model was able to identify the structural features on the cations and anions that had the greatest effect on the rate constant. Secondly, predictive MLREM and BRANNLP models were developed from the full initial set of rate constant data. From these models, a large number of predictions (>9000) of rate constant were made for mixtures of different ionic liquids, at different proportions of ionic liquid and molecular solvent, at different temperatures. A selection of these predictions were tested experimentally, including through the preparation of novel ionic liquids, with overall good agreement between the predicted and experimental data. This study highlights the benefits of using machine learning methods on kinetic data in ionic liquid mixtures to enable the development of rigorous structure-property relationships across multiple variables simultaneously, and to predict properties of new ILs and experimental conditions.

Prediction of Solvation Free Energies of Ionic Solutes in Neutral Solvents

The prediction of solvation free energies is essential for a variety of applications. Solvation free energies of neutral systems can be predicted quite accurately. The accuracy of predictions for solvation free energies of ionic solutes dissolved in neutral solvents, however, has been reported to be worse by at least 1 order of magnitude. In this study, the performance of three approaches for solvation free energy prediction of several hundred ions dissolved in neutral solvents is evaluated. The applied methods are COSMO-RS, cluster continuum model (CCM) together with COSMO-RS, and COSMO-RS-ES. It is emphasized that the reference data for model evaluation are subject to large uncertainties stemming from the impossibility to measure the so-called elusive absolute free energies of solvation of a single ion. Consequently, such uncertainty must be considered during the evaluation of prediction methods. Therefore, a straightforward approach to account for the underlying uncertainty is applied here. Hereby, it is revealed that the true performance of the method is better than what is often reported. The average absolute deviation (AAD) of COSMO-RS is calculated to be 2.3 kcal mol^-1, while applying the CCM and COSMO-RS-ES each results in AADs of 2.0 kcal mol^-1. This accuracy allows for qualitative assessment of solvation free energy-dependent quantities, such as reaction rate constants.