  Compositional disorder induces myriad captivating phenomena in perovskites.
Target-driven discovery of perovskite solid solutions has been a great
challenge due to the analytical complexity introduced by disorder. Here, we
demonstrate that an unsupervised deep learning strategy can find fingerprints
of disordered materials that embed perovskite formability and underlying
crystal structure information by learning only from the chemical composition,
manifested in (A1-xA'x)BO3 and A(B1-xB'x)O3 formulae. This phenomenon can be
capitalized to predict the crystal symmetry of experimental compositions,
outperforming several supervised machine learning (ML) algorithms. The educated
nature of material fingerprints has led to the conception of analogical
materials discovery that facilitates inverse exploration of promising
perovskites based on similarity investigation with known materials. The search
space of unstudied perovskites is screened from ~600,000 feasible compounds
using experimental data powered ML models and automated web mining tools at a
94% success rate. This concept further provides insights on possible phase
transitions and computational modelling of complex compositions. The proposed
quantitative analysis of materials analogies is expected to bridge the gap
between the existing materials literature and the undiscovered terrain.
