Pre‐Refinement Prediction of Dopant Site Occupancy in M‐Type Hexaferrites via Hierarchical and Uncertainty‐Aware Machine‐Learning

ABSTRACT

Dopant site occupancy bridges chemical composition, local crystal structure, and macroscopic functional responses in M‐type hexaferrites. However, reliable occupancy hypotheses are difficult to establish before structural refinement. Here, we develop a pre‐refinement machine‐learning framework using 494 dopant‐level records extracted from refined structures. This workflow combines Ba/Sr‐vs.‐Fe sublattice classification, fractional occupancy prediction over five inequivalent Fe sites, and reliability‐aware assessment. The CatBoost classifier achieves 96% accuracy on external validation for sublattice assignment. For Fe‐sublattice dopants, homogeneous occupancy‐model screening and heterogeneous cooperative occupancy‐model screening yield a macro mean absolute error as low as 0.0584, while preserving dominant‐site identification and occupancy ranking. The results show that dopant site occupancy is jointly controlled by dopant chemistry, target‐dopant amount, and co‐doping environment rather than a universal site‐preference rule. By integrating TabPFN baseline comparison, training‐experience assessment, and model‐consistency analysis, the framework identifies boundary and extrapolative samples that require priority refinement. Our predictions for W‐doped and Sc‐doped M‐type hexaferrites successfully reproduce key experimental occupancy trends. This work offers a practical data‐driven strategy to generate physically plausible occupancy hypotheses and assign refinement priorities for partially occupied oxide systems.