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This review systematically examines the handling—or more often the neglect—of domain shift within the materials machine learning literature published between 2017 and 2023, drawing on a targeted search of peer-reviewed publications across specialized databases and journals to compile and analyze exactly 30 representative studies that span foundational overviews, application-focused works, and methodological explorations. Domain shift in materials science takes four distinct yet interrelated forms—temporal, compositional, experimental, and theoretical—each arising from the inherently heterogeneous nature of materials data sources that range from evolving laboratory protocols and diverse chemical families to inter-laboratory variations and discrepancies between computational approximations and experimental realities. Current practices reveal that explicit acknowledgment of domain shift remains rare, with the majority of papers proceeding under the default assumption of identical training and test distributions. At the same time, detection methods and adaptation strategies appear in fewer than one in five studies, leaving models vulnerable to silent degradation when deployed on real-world materials problems. The surveyed methods for handling domain shift include statistical detection techniques, domain-adversarial training frameworks, feature-alignment approaches, and shift-robust evaluation protocols, many of which have been proposed in adjacent machine-learning fields yet remain underutilized in materials contexts despite their direct relevance to property prediction and inverse design tasks. Collectively, these findings underscore the urgent need for standardized shift-reporting protocols, the development of materials-specific out-of-distribution benchmarks, and the integration of domain-adaptation pipelines into routine workflows, thereby elevating the reliability, generalizability, and practical utility of machine-learning models in accelerating materials discovery.

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.

Out-of-distribution (OOD) generalization remains one of the most pressing barriers to the reliable deployment of artificial intelligence in materials science. Although machine-learning models now routinely achieve sub-0.1 eV/atom errors on in-distribution test sets for formation energies, band gaps, and elastic moduli, these same models frequently collapse when confronted with materials that lie outside the training distribution. Real-world materials discovery and process optimization demand predictions for unseen compositions, novel crystal prototypes, altered thermodynamic conditions, and non-equilibrium dynamical regimes—scenarios that constitute domain shift rather than simple interpolation. This systematic review synthesizes the literature on OOD generalization in materials AI drawing exclusively peer-reviewed publications from high-impact venues including npj Computational Materials, Digital Discovery, Machine Learning: Science and Technology, and Journal of Chemical Theory and Computation. We identify four primary types of domain shift—compositional, structural, thermodynamic, and dynamical—and introduce a fifth multi-dimensional category that captures the realistic superposition of shifts encountered in practice. Current methodological families are critically assessed: domain adaptation (distribution alignment), invariant learning (IRM and Group DRO), physics-informed and symmetry-aware data augmentation, uncertainty quantification for OOD detection, and extrapolation-aware architectures (equivariant networks, multi-fidelity models, and generative priors). Empirical findings across the corpus reveal a consistent pattern: modest gains (20–50 % error reduction) are achievable for small, single-axis shifts, yet performance degrades sharply—and often catastrophically—for large compositional jumps, prototype changes, or combined multi-dimensional shifts. No method currently delivers reliable extrapolation beyond the convex hull of the training manifold. Key gaps persist. The community lacks standardized OOD benchmarks with controlled shift axes, theoretical guarantees for extrapolation remain underdeveloped, and conditional (per-input) robustness guarantees are almost entirely absent. Evaluation metrics are inconsistent, rendering cross-paper comparisons unreliable. This review therefore provides not only a taxonomy and synthesis but also a forward-looking identification of unsolved problems that must be addressed before materials AI can transition from laboratory demonstration to industrial reliability. OOD generalization in materials AI is no longer an optional research direction; it is the central unsolved challenge that will determine the field’s practical impact over the next decade.

The advent of computational and data-driven materials engineering has revolutionized the discovery and design of advanced materials, leveraging machine learning to navigate vast chemical spaces and predict properties from multimodal datasets. However, a critical challenge persists in the form of domain shifts, where AI models trained on one material class exhibit diminished predictive accuracy when inferred across disparate materials, undermining transferability in cross-material inference scenarios. This conceptual manuscript addresses this gap by introducing a novel framework that dissects the epistemic and computational underpinnings of such shifts within materials informatics ecosystems. Drawing from representation learning, graph neural networks, and uncertainty quantification paradigms, the proposed Cross-Material Inference Cascade (CMIC) framework conceptualizes domain shifts as emergent from mismatched representational hierarchies and inference pipelines, rather than mere data scarcity. It outlines structural layers for mitigating these shifts through adaptive representation alignments and feedback-driven discovery logics, without relying on empirical transfer learning techniques. Implications extend to high-throughput computation, autonomous discovery systems, and inverse design, fostering more resilient AI infrastructures in materials science. By emphasizing computational workflow dynamics and epistemic risk structures, this work provides interpretive insights for steering future data-driven paradigms toward robust cross-material predictions, enhancing the interoperability of foundation models and simulation-experiment couplings in the field.