The training of the ML model (II) can be executed interactively with the IPython notebook labeled "02" or using a non-graphic learn-curve generator script. The tasks performed by notebook "02" start by parsing the QC program outputs of the training set and storing them in a condensed raw data file. This process (internally referred to as merging) has the purpose of collecting all relevant data for producing descriptors in a single compact file for each molecule -thus speeding up all following look-up processes, whenever the descriptor data would need to be fetched from scattered files. Further, this also allows to delete the much larger original output files afterwards -thus drastically reducing the hard disk space required for possibly millions of molecules.