  Screening combinatorial space for novel materials - such as perovskite-like
ones for photovoltaics - has resulted in a high amount of simulated
high-troughput data and analysis thereof. This study proposes a comprehensive
comparison of structural-fingerprint based machine-learning models on seven
open-source databases of perovskite-like materials to predict bandgaps and
energies. It shows that none of the given methods are able to capture arbitrary
databases evenly, while underlining that commonly used metrics are highly
database dependent in typical workflows. In addition the applicability of
variance selection and autoencoders to significantly reduce fingerprint size
indicates that models built with common fingerprints only rely on a submanifold
of the available fingerprint space.
