Not yet reviewed by Pith; the record is open.

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.