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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.

The integration of artificial intelligence into materials science has accelerated discovery through iterative workflows that cycle through data acquisition, model refinement, prediction, explanation, and hypothesis-driven experimentation. While explainable artificial intelligence (XAI) methods enhance trust and scientific insight by elucidating model decisions, these explanations are not static. This manuscript introduces the novel concept of explainability drift: the systematic degradation, inconsistency, or divergence in the fidelity, stability, and relevance of XAI-generated explanations across successive iterations of materials AI workflows. Distinct from prediction-focused concept drift, explainability drift arises from evolving data distributions, model updates, feature space expansions, and domain shifts inherent to materials exploration. Through a purely conceptual failure analysis, we delineate the mechanisms underlying explainability drift, including temporal instability in feature attributions, erosion of surrogate model alignment, and semantic misalignment between explanations and emerging material knowledge. Drawing on recent peer-reviewed advances in XAI applications to property prediction, microstructure analysis, and generative design, we synthesize theoretical foundations to highlight why drift undermines iterative efficacy. The proposed conceptual framework organizes explainability drift into multidimensional layers—attributional, structural, and epistemic—offering a structured lens for analyzing failure modes without empirical validation. This framework emphasizes risks such as misguided hypothesis generation, diminished trust in AI-assisted insights, and inefficient navigation of vast materials design spaces. By conceptualizing explainability drift as an intrinsic challenge, the work advocates for theoretical advancements in sustained explainability to support robust, interpretable AI-driven materials innovation.

In the evolving landscape of computational and data-driven materials engineering, iterative learning systems have become pivotal for accelerating materials discovery through integrated machine learning pipelines and high-throughput computations. These systems, encompassing active learning loops and closed-loop experimentation, rely on dynamic representations of materials properties and structures to guide successive iterations of model refinement and data acquisition. However, a critical yet underexplored phenomenon emerges: representation drift, where iterative updates inadvertently alter the semantic fidelity of learned embeddings, potentially leading to misaligned inferences across discovery cycles. This conceptual manuscript identifies this gap within materials informatics ecosystems, highlighting how drift manifests in graph neural networks, multimodal datasets, and uncertainty-aware frameworks. To address this, we introduce the Iterative Representation Stabilization Framework (IRSF), a novel conceptual architecture that integrates stabilization mechanisms across data ingestion, model adaptation, and inference steering layers. IRSF conceptualizes drift as a systemic interaction between feedback loops and representation spaces, offering interpretive insights into maintaining epistemic consistency in autonomous discovery workflows. Implications extend to enhancing the robustness of foundation models for science, simulation-experiment couplings, and inverse design paradigms, fostering more reliable computational steering in materials engineering. By framing representation drift through infrastructure-level trade-offs, this work provides a foundational lens for interpreting iterative dynamics, ultimately supporting sustainable advancements in data-driven materials paradigms.