Viscosity model of deep eutectic solvents from group contribution method

In this work, based on mathematical model inspired by transition state theory, the group contribution (GC) method is used to predict the viscosity of DESs. The model is constrained by Eyring rate theory and hard sphere free volume theory. A dataset of 2229 experimental measurements of the viscosity of 183 DESs from literature is used for determining the model parameters and subsequent verification of the model. The rules introduced by this model are simple and easy to understand. The results show that the proposed model is able to predict the DESs viscosity with very high accuracy, i.e., with an average absolute relative deviation of 8.12% over the training set and 8.64% over the test set, using only temperature and composition as inputs. The maximum absolute relative deviation is 34.63%. Therefore, the as-proposed model can be considered a highly reliable tool for predicting DESs viscosity when experimental data are absent.

Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy

We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) to predict the Abraham solute parameters, as well as a machine learning model (DirectML) to predict solvation free energy and enthalpy at 298 K. The proposed group contribution method uses atom-centered functional groups with corrections for ring and polycyclic strain whilst the machine learning models adopt a directed message passing neural network. The solute parameters predicted from SoluteGC and SoluteML are used to calculate solvation energy and enthalpy via linear free energy relationships. Extensive data sets containing 8366 solute parameters, 20253 solvation free energies, and 6322 solvation enthalpies are compiled in this work to train the models. The three models are each evaluated on the same test sets using both random and substructure-based solute splits for solvation energy and enthalpy predictions. The results show that the DirectML model is superior to the SoluteML and SoluteGC models for both predictions and can provide accuracy comparable to that of advanced quantum chemistry methods. Yet, even though the DirectML model performs better in general, all three models are useful for various purposes. Uncertain predicted values can be identified by comparing the 3 models, and when the 3 models are combined together, they can provide even more accurate predictions than any one of them individually. Finally, we present our compiled solute parameter, solvation energy, and solvation enthalpy databases (SoluteDB, dGsolvDBx, dHsolvDB) and provide public access to our final prediction models through a simple web-based tool, software package, and source code.

Group Contribution Revisited: The Enthalpy of Formation of Organic Compounds with “Chemical Accuracy” Part II

Group contribution (GC) methods to predict thermochemical properties are eminently important to process design. We present a group contribution parametrization for the heat of formation of organic molecules exhibiting chemical accuracy, maximum 1 kcal/mol (4.2 kJ/mol) difference between experiment and model values while minimizing the number of parameters avoiding overfitting and therewith avoiding reduced predictability. Compared to the contemporary literature, this was successfully achieved by employing available literature high-quality and consistent experimental data, optimizing parameters group by group, and introducing additional parameters when chemical understanding was obtained supporting these. A further important result is the observation that the applicability of the group contribution approach breaks down with increasing substitution levels, i.e., more heavily alkyl-substituted molecules, the reason being a serious influence of substitution on the conformation of the flexible part of the entire molecule within particular valence angles and torsional angles affected, which cannot be accounted for by additional GC parameters with fixed numerical values.

Estimating Activity Coefficients of Target Components in Poorly Specified Mixtures with NMR Spectroscopy and COSMO-RS

Poorly specified mixtures are common in process engineering, especially in bioprocess engineering. The properties of such mixtures of unknown composition cannot be described using conventional thermodynamic models. The NEAT method, which has recently been developed in our group, enables the calculation of activity coefficients of known target components in such poorly specified mixtures. In NEAT, the group composition of the mixture is determined by NMR spectroscopy and a thermodynamic group contribution method is used for calculating the activity coefficients. In all previous studies with NEAT, the UNIFAC group contribution method was used. In the present work, we demonstrate that NEAT can also be applied with another important method for predicting activity coefficients: COSMO-RS. COSMO-RS (OL) developed in Oldenburg together with its group contribution version GC-COSMO-RS (OL) is used here. The new version of NEAT was successfully tested. For a variety of aqueous mixtures excellent agreement of the NEAT predictions, for which only information on the target component was used, with results that were obtained using the full knowledge on the composition of the mixture was found. The results demonstrate the generic nature of the idea of NEAT and the broad applicability of the method.