Thermodynamic data are key in the understanding and design of chemical processes. Next to the experimental evaluation of such data, computational methods are valuable and sometimes indispensable tools in obtaining heats of formation and Gibbs free energies. The major toolboxes to obtain such quantities by computation are quantum mechanical methods and group contribution methods. Although a lot of progress was made over the last decade, for the majority of chemical species we are still quite a bit away from what is often referred to as chemical accuracy, i.e. ‘1 kcal mol⁻¹’. Currently, for larger molecules the combination of group contribution methods with group additive values that are determined with the best available computational ab initio methods seems to be a viable alternative to obtain thermodynamic properties near chemical accuracy. New developments and full use of existing tools may lead to further improvements (critical review, 83 references).

Group contribution (GC) methods to predict thermochemical properties are of eminent importance to process design. Compared to previous works, we present an improved group contribution parametrization for the heat of formation of organic molecules exhibiting chemical accuracy, i.e., a maximum 1 kcal/mol (4.2 kJ/mol) difference between the experiment and model, while, at the same time, minimizing the number of parameters. The latter is extremely important as too many parameters lead to overfitting and, therewith, to more or less serious incorrect predictions for molecules that were not within the data set used for parametrization. Moreover, it was found to be important to explicitly account for common chemical knowledge, e.g., geminal effects or ring strain. The group-related parameters were determined step-wise: first, alkanes only, and then only one additional group in the next class of molecules. This ensures unique and optimal parameter values for each chemical group. All data will be made available, enabling other researchers to extend the set to other classes of molecules.

Physicochemical properties of chemicals as referred to in this review include, for example, thermodynamic properties such as heat of formation, boiling point, toxicity of molecules and the fate of molecules whenever undergoing or accelerating (catalytic) a chemical reaction and therewith about chemical equilibrium, that is, the equilibrium in chemical reactions. All such properties have been predicted in literature by a variety of methods. However, for the experimental scientist for whom such predictions are of relevance, the accuracies are often far from sufficient for reliable application We discuss current practices and suggest how one could arrive at better, that is sufficiently accurate and reliable, predictive methods. Some recently published examples have shown this to be possible in practical cases. In summary, this review focuses on methodologies to obtain the required accuracies for the chemical practitioner and process technologist designing chemical processes. Finally, something almost never explicitly mentioned is the fact that whereas for some practical cases very accurate predictions are required, for other cases a qualitatively correct picture with relatively low correlation coefficients can be sufficient as a valuable predictive tool. Requirements for acceptable predictive methods can therefore be significantly different depending on the actual application, which are illustrated using real-life examples, primarily with industrial relevance. Furthermore, for specific properties such as the octanol-water partition coefficient more close collaboration between research groups using different methods would greatly facilitate progress in the field of predictive modelling.

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.