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This paper addresses the reliability of AI/ML for magnetic materials discovery by analyzing database completeness and physical validity, particularly concerning competing magnetic states, disorder, and finite-temperature effects. A hierarchical workflow integrating multiple ML models with explicit electronic-structure, magnetic, dynamical, thermodynamic, kinetic, and experimental validation is proposed to overcome these limitations. The introduction of a synthesis-window descriptor, exemplified by Fe-Co-B, highlights that kinetically accessible materials, not just energetically favored ones, are crucial for reliable AI-driven discovery.
Magnetic materials discovery via AI/ML is fundamentally flawed if it overlooks kinetic accessibility and finite-temperature effects, as demonstrated by Fe-Co-B's ordering temperature falling below room temperature despite thermodynamic favorability.
The rapid growth of AI/ML methods for materials discovery has increased the importance of the quality, physical completeness, and information content of computational databases. We examine these issues for magnetic materials, where incomplete sampling of competing magnetic states, chemical disorder, and finite-temperature properties can lead to apparently accurate but physically unreliable predictions. We propose a hierarchical discovery workflow combining independent ML models with explicit electronic-structure, magnetic, dynamical, thermodynamic, kinetic, and experimental validation. To incorporate kinetic accessibility into high-throughput screening, we introduce a dimensionless synthesis-window descriptor based on the relation between the chemical order-disorder and Tammann temperatures. A cluster-expansion analysis of Fe-Co-B illustrates the importance of this criterion: although ordered Fe3CoB2 is energetically favored at T=0, its small ordering energy produces an order-disorder temperature far below room temperature, making long-range Fe/Co ordering kinetically unachievable. These results demonstrate that reliable AI/ML discovery of magnetic materials requires databases and validation procedures that contain physical information relevant to the target functionality, rather than simply larger numbers of calculated structures.