###### Abstract
This result could be obtained because some distinct aspects/approaches were taken into account: (i) using accurate and reliable experimental data, because as the GC method is a so-called data-driven model with experimental data being used to parametrize the model, our self-imposed requirement of chemical accuracy means that the quality of the experimental data is preeminent; (ii) optimizing parameters Group by Group and introducing an absolute minimum number of additional Group-specific parameters related to nearest or next-nearest neighbour interactions; (iii) the recognition of the limitations of the GC approach, i.e., the breakdown related to the conditions of linearity and additivity that can arise as a result of steric hindrance, ring strain, geminal effects, or electronic conjugation effects; and (iv) the judicious definition of chemical groups, e.g., a phenyl Group rather than six individual aromatic carbon atoms. Most GC approaches reported in the past have adopted small chemical groups, e.g., CH\({}_{3}\), CH, etc., and applying a linear additive method does not lead to a generic GC method with chemical accuracy. For many of the classes of molecules we have reviewed in the previous four papers, the Group Contribution concept worked very well, as we also know from the literature. Comparatively small additional effects could be treated as a correction, but larger effects require the choice of larger entities as Groups when the aim is to achieve high chemical accuracy. In our previous work, we have presented the example of 1,3-dioxolane acetals, which revealed that adopting the appropriate size of a Group representing the acetal leads to a model revealing chemical or close to chemical accuracy. Similar good results were obtained when adopting quinoline and pyridine as individual Groups. Finally, the choice of small Groups as practiced in other works, accompanied by the introduction of higher order Groups' contribution as in Refs. [10; 13; 14], leads to a very large number of additional parameters, as there are many triple-group combinations, and, consequently, the problem of overfitting occurs (in essence, such approaches are group-interaction models for which it is generally known that many more parameters are involved). For more details, like the relative number of parameters, see Ref. [4]. Our recent results [1; 2; 3; 4] outperform previous parametrizations and show both a low absolute average deviation between experimental and model values and, compared to other GC models, exceptionally few outliers. The latter is crucial for reliable process design. When we take into account chemical knowledge (the practical knowledge about molecules that chemists have collected over many decades), we know, for instance, that there is a redistribution of the electron density in heterocycles, which is different between a phenyl ring having a single N substitution (pyridine), two (pyrimidine), or three (triazine). The fact, and this is very important, that we have a non-discrete scale for electron distribution, therewith for bond strength and therewith for the heat of formation, inevitably implies the additive character whilst adopting the smallest of Groups, i.e., CH\({}_{3}\), CH\({}_{2}\), aromatic CH, etc., cannot hold. To achieve wide applicability to a larger variety of organic molecules, further Groups should be parametrized. But for that to be possible under the constraint of chemical accuracy, we need many more experimental data which, however, are scarce. Alternatively, ab initio quantum chemical calculations can be used to obtain heats of formation. The high-level calculations required cannot be applied to larger molecules, due to the excessive computational expense, but calculations on smaller entities can be used to determine additional Group Contribution parameters. There is a range of references in the literature supporting that the G4 quantum method [16; 17; 18] provides good results for the heat of formation of organic molecules [19; 20; 21; 22; 23].