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The integration of artificial intelligence (AI) and machine learning (ML) in materials science has accelerated discovery and design processes, yet it introduces challenges related to failure, uncertainty, and risk. This narrative review examines how the materials AI literature addresses negative outcomes, including model uncertainties, predictive failures, and associated risks in application. Drawing on peer-reviewed studies, we explore uncertainty quantification techniques, robustness evaluations, and risk mitigation strategies. Key themes include Bayesian methods for uncertainty estimation, benchmark studies on prediction reliability, and strategies to handle data scarcity and extrapolation errors. The review highlights gaps in handling adversarial conditions and real-world failures, proposing future directions for more resilient AI frameworks in materials research. By synthesizing these insights, we aim to foster a more cautious and effective use of AI in advancing materials innovation.

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.