Another interesting observation that arises from table 2 is the low weight resulting for Li 15 Si 4 , in comparison with the other alloys of each set. Furthermore, in the case of set B this weight is practically zero, indicating that the inclusion of these structures in the training dataset does not contribute to improving the overall prediction of the relative formation energies. This leads us to assume that Li 15 Si 4 structure has a particular nature that differs from all the other alloys and interferes somehow with the fitting of the model parameters. For instance, a plausible explanation may be that the chemical environment of this structure is quite different from the rest of the alloys so the prediction can be globally improved by focusing the model on all the other alloys but not Li 15 Si 4 . In this sense, the weight optimization procedure proposed in this work may be seen as a tool to detect peculiar information introduced in a training dataset. This should be addressed in further work.