## 2 Accuracy of Predictive Methods and What Is Required
### Validation  
Validation is a key item in any modelling exercise and should be associated to any modelling method, molecular or macroscopic modelling. We have been discussing different methods in this paper and these generally involve different methods of validation.  
For ab initio methods, about three approaches are practiced. There is quite a few papers where ab initio data are published without a validation. Still some researchers arguing on the basis that ab initio is from first principles are therewith good. The second approach is to use some reference data on similar systems which serve as calibration for the ab initio results of new but similar systems. When it is about general property prediction, as we primarily discuss in the present paper, reference sets of molecules have been proposed and used for the purpose of validation. One example is the G2 set proposed by [PERSON] et al. [42]. This is a good and valid procedure, however these sets consist of really small molecules only and this is really small, so no real-life relevant molecules for industry or academic research into larger organic or organometallic species. This means we lack proper validation for most molecules. As the error in ab initio calculations is largely at the total energy of the atom level, the error increases with the size of the molecule and we have no proper referencing. Of course one could alleviate this problem but that needs a much larger data base band massive quantum computations.  
For the statistical and neural network based models the validation is commonly done using a training set and a test set. We have a set of molecules and for the property of interest also experimental data exist for comparison. A possibly arbitrary subset is taken out of the data set. With arbitrary it is meant that those taken out contain typical groups, double bonds and so forth, resembling the molecules in the larger training set. This is to guarantee that when a good predictive model is constructed for the training set and that model is subsequently applied to the test set, also for the test set we will obtain good results. In order to further improve and guarantee good predictivity one may repeat this process with other test sets, which is a method known as cross-validation. These approaches are generally applied in statistical an neural network based modelling as there is no a priori physical meaning of the correlations obtained. It is in essence all purely mathematical, though these models can have very good predictive properties.  
Finally, for the Group Contribution methods a similar validation procedure involving a test set has been practiced [22]. In practice the model is commonly developed on a larger data set, for example thousands of compounds, primarily organic species. We know that many properties are additive to some or a large extent. Higher order groups (neighbour effects) are introduced to correct for deviations due to specific neighbouring.  
Is There a Way forward towards Reliable Predictive Methods for Physicochemical Property Prediction of Chemicals? How to Arrive at Good Methods for the Prediction of Physicochemical Properties