Prediction‐Guided Two‐Step Solid‐State Exploration of Unknown Pseudo‐Ternary Oxides

Multicomponent oxides are important materials for energy, catalytic, and electronic applications, but their huge composition space makes exploration by conventional solid‐state synthesis inefficient. Machine learning‐based selection is combined with a two‐step experimental workflow for pseudo‐ternary oxide discovery. Pseudo‐ternary systems with no registered compositions in an Inorganic Crystal Structure Database are ranked by the average prediction score of a model trained only on pseudo‐binary oxides, and the top 300 systems are screened by a slurry‐based solid‐state reaction method suited for robotic dispensing. Among the 300 tested compositions, 21 show diffraction peaks that cannot be explained only by simple or binary oxides. Three match ternary oxide phases in a powder diffraction database, 15 suggest cation‐disordered ternary phases, and two copper‐containing samples form oxides with copper valence states different from those assumed in the prediction. One remaining composition yields a new oxide, Ba 5 SnV 6 O 22 . Structural analysis shows that this phase has a Ba 2 BiV 3 O 11 ‐related framework, in which the Bi‐equivalent site is randomly occupied by Sn and Ba. These results show that prediction‐guided selection and a robotics‐compatible synthesis workflow can work together for oxide discovery.