  We present a physically motivated strategy for the construction of training
sets for transferable machine learning interatomic potentials. It is based on a
systematic exploration of all possible space groups in random crystal
structures, together with deformations of cell shape, size, and atomic
positions. The resulting potentials turn out to be unbiased and generically
applicable to studies of bulk defects without including any defect structures
in the training set or employing any additional Active Learning. Using this
approach we construct transferable potentials for pure Magnesium that reproduce
the properties of hexagonal closed packed (hcp) and body centered cubic (bcc)
polymorphs very well. In the process we investigate how different types of
training structures impact the properties and the predictive power of the
resulting potential.
