## 4 Results
### AD Definition during ML Model Construction (Substudy 2)
#### 4.2.3 On the Understanding of the High Prediction Errors for Test Molecules  
To provide further understanding of the high prediction errors of some test molecules, tSNE 2D is employed to visualize the data in a lower dimensional space. Figures 15a,d and 16a,d correspond to the tSNE 2D representation of splits 2-5 for enthalpy. Figures 15b,e and 16b,e reproduce the previously mentioned subfigures (Figures 15a,d and 16a,d) but while highlighting a different distinctive characteristic of the plotted points. More specifically, these subfigures separate the molecules by chemical family, while those of Figures 15c,f and 16c,f display the reference enthalpy values from the DIPPR database. In all the subfigures, the points with black contours correspond to the test data. The idea behind using all these representations is to find the source of high prediction errors for some test molecules. The hypotheses that are tested here are that these molecules are either located in a low-density area, in terms of the training descriptor space, or they are located in a high-density area but with a high variation in the training property values. Note that tSNE 2D representations of splits 2, 4 and 5 for the entropy are provided in Figures A3 and A4. Also, for Figures 15b,e, 16b,e, A3b,e and A4b, the color codes corresponding to different families are detailed in Figure A5. First of all, it is noticeable that the tSNE 2D representation creates some clusters or regions which contain molecules of certain chemical families and/or have property values within a certain range. Indeed, the principle of tSNE is based on the conservation of the local structure of the data, meaning that similar data in a high-dimensional space will be plotted in a close vicinity in a lower-dimensional space. However, the region boundaries are not always clearly delimited and some points do not belong to clusters displaying the above characteristics. Several hypotheses could be put forward to explain this. First of all, the dimensional \"compression\" of a 100D space to a 2D one could be too high to allow for a clear data clustering. Another reason for the high diversity of chemical families that is observed in some of the formed clusters could be the fact that the selected descriptors (or even the use of molecular descriptors in general) leads to a classification of the molecules that is not completely consistent with their chemical classification in the established chemical families. In other words, molecules may be separated according to their similarities in terms of some descriptor values that would result in new families containing, for example, some alkanes and some esters. Finally, the shape of the clusters in tSNE representation is also highly dependent on the tSNE hyperparameter values, such as the perplexity, which is related to the number of nearest neighbors each point has [56; 74]. Figure 15: GNE 2D representations for **enthalpy** after outlier elimination (Layout 3) and dimensionality reduction (GA), **splits 2 and 3**. Subfigures (**a**) and (**d**) show the original data, distinguishing train and test sets. Subfigures (**b**) and (**e**) show the same respective data, distinguishing chemical families (color codes given in Figure 14). Subfigures (**c**) and (**f**) show the same respective data, distinguishing the reference H values from the DIPPR. Figure 16: GNE 2D representations for **enthalpy** after outlier elimination (Layout 3) and dimensionality reduction (GA), **splits 4 and 5**. Subfigures (**a**) and (**d**) show the original data, distinguishing train and test sets. Subfigures (**b**) and (**e**) show the same respective data, distinguishing chemical families (color codes given in Figure 10). Subfigures (**c**) and (**f**) show the same respective data, distinguishing the reference H values from the DIPPR. Secondly, the combination of tSNE representations permits us to highlight the potential reasons for the observed high prediction errors of some test molecules.