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Out-of-distribution (OOD) generalization has become a central claim in machine learning for materials discovery, yet its meaning remains unstable when crystalline materials are represented as graphs. In current practice, the term is applied to qualitatively different forms of domain shift without specifying which properties of the training support have actually been violated, rendering many OOD claims difficult to verify or compare. This article addresses that conceptual gap through a boundary-focused analysis of crystalline graph learning. It shows that prevailing usage conflates multiple non-equivalent shifts and argues that conventional vector-space definitions of OOD are inadequate for periodic, graph-structured materials data. In response, the paper identifies four primary dimensions along which crystalline graph distributions depart from training support: composition, structure, scale, and condition. It then proposes a dimension-explicit redefinition of OOD, together with measurable boundary criteria, operational detection rules, and a per-dimension domain-shift score that can be computed from characterized training distributions. Boundary cases and gray zones are examined to clarify how formally defined thresholds should be interpreted in practice. By distinguishing OOD from anomaly detection, novelty detection, extrapolation, and domain adaptation, the framework establishes a more precise conceptual foundation for evaluating generalization in crystalline materials machine learning. The central contribution is not a new predictive model, but a falsifiable vocabulary for reporting domain shift. Adopting dimension-specific OOD reporting would make claims of robustness more reproducible, benchmark design more informative, and model evaluation more scientifically defensible in AI-driven materials discovery.