  The structural tunability and compositional diversity of multicomponent
perovskite oxides have enabled their various applications, including catalysis
and electronics. The cation ordering in these oxides, ranging from disordered
(i.e., high-entropy) to ordered (e.g., rocksalt), profoundly influences their
properties. While computational design tools can typically predict properties
associated with a particular ordering, inferring which ordering -- if any --
will be observed in synthesized oxides remains challenging. Here, we leveraged
first-principles simulations and machine learning to develop data-driven,
physics-informed descriptors of experimental ordering in multicomponent
perovskites and compared them with traditional physicochemical descriptors,
e.g., ionic radii and oxidation states. The fitted low-dimensional
classification models correctly rank up to 93% of compositions in an
experimental dataset of 190 perovskites between cation-ordered and disordered,
offering a rigorous benchmark between theory and experiments. Furthermore,
these descriptors accelerate high-throughput virtual screening of
multicomponent oxides by predicting their dominant ordering to avoid costly,
exhaustive simulations of cation arrangements.
