  The optimization of properties of perovskite oxides has drawn interest on
account of their diverse areas of application. In this work, the hierarchical
clustering technique is used to reduce the multi-collinearity among selected
features from literature that are reported to have an effect on perovskite
formation and stability. Operating on the vast composition space of double
oxide perovskite compositions available in literature and online repositories,
in this manuscript, an attempt has been made to extract the relationship
between the composition and structure to predict their formability and
stability. Machine learning (ML) classifiers are trained on these datasets to
predict novel stable perovskite compositions. The study uses a vast feature
space to narrow down the most important factors affecting the formability and
stability in perovskite compounds. It also identifies stable compositions that
have band gaps suitable for photovoltaic and photocatalytic applications. The
developed random forest (RF)-based models may be extended to include the
implications beyond photosensitive applications by focusing on the
physico-chemical mechanisms driving the phenomena behind each application.
