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Artificial intelligence is rapidly reshaping materials science by accelerating property prediction, synthesis planning, and materials design. Yet most AI models for materials are developed and validated under implicit stationary assumptions, while real deployments unfold in time-varying environments where materials, sensors, and processes evolve. This review synthesizes what is currently known about temporal generalization in materials AI—the capacity of models to remain reliable as data distributions and underlying mechanisms change. We distinguish two dominant degradation pathways: drift, in which input statistics or input–output relationships shift over time, and model aging, in which learned representations become obsolete as systems evolve. Drawing on evidence across biosensing and wearables, electrochemical energy storage, polymer synthesis, automated laboratories, and industrial manufacturing, we summarize how temporal failures arise, how they are detected, and why they often remain silent until performance drops become consequential. We then evaluate mitigation strategies—including domain adaptation, incremental and continual learning, active data acquisition, uncertainty-aware prediction, and human–AI feedback loops—highlighting where they succeed, where they break down, and the constraints that limit their scalability in real-world settings. Finally, we identify key gaps: limited longitudinal datasets, weak standardization of temporal evaluation protocols, underexplored multimodal temporal fusion, and insufficient emphasis on prevention rather than detection. We conclude with a forward agenda for resilient materials AI built around lifecycle monitoring, benchmarkable temporal stress tests, and hybrid frameworks that integrate mechanistic knowledge with adaptive learning to sustain reliability over time.

In the rapidly expanding domain of artificial intelligence applied to materials science, a persistent conceptual ambiguity undermines the reliability of reported model capabilities. The terms “generalization” and “transfer” are routinely conflated, with authors claiming that a model “generalizes” when it is in fact being evaluated on samples drawn from a distinctly different distribution. This boundary/definitional paper draws a sharp conceptual distinction between the two notions. Generalization is defined as the expected performance of a trained model on new samples drawn independently and identically from the same underlying distribution as the training data. In contrast, transfer is defined as performance on samples drawn from a different distribution, where the I.I.D. assumption is violated by construction. The distinction matters because a model that generalizes excellently within its training distribution can fail dramatically under transfer conditions, and conversely, a successful transfer mechanism may mask poor generalization; treating the two interchangeably, therefore, produces overclaims about model robustness that cannot be sustained when materials discovery moves beyond the convex hull of available training data. The paper articulates a two-dimensional boundary framework—distribution-shift magnitude and feature-space overlap—that locates any given evaluation setting along a continuum from pure generalization to pure transfer, thereby enabling authors, reviewers, and practitioners to specify precisely which capability is being claimed and tested. By clarifying these boundaries and exposing the epistemic costs of current usage, the work supplies a conceptual foundation for more disciplined reporting standards and evaluation protocols in materials machine learning.