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The integration of artificial intelligence (AI) and machine learning (ML) into materials science has accelerated the discovery and design of novel materials by enabling high-throughput prediction of properties from composition, structure, and processing parameters. However, the reliability of these predictions is frequently compromised by uncertainties stemming from limited datasets, model approximations, experimental noise, and intrinsic variability in materials systems. This narrative review synthesizes recent advances in understanding uncertainty and reliability in materials AI. It covers fundamental concepts such as aleatoric and epistemic uncertainty; methods for quantification, including Bayesian neural networks, ensembles, and Gaussian processes; inconsistencies in terminology and language across the literature; and the downstream consequences for decision-making in materials engineering, design, and deployment. Emphasis is placed on calibration of uncertainty estimates, domain-of-applicability assessment, and risk-aware applications in safety-critical contexts such as structural alloys and energy materials. By highlighting best practices and gaps, the review advocates for standardized frameworks to build trust and facilitate industrial translation of materials AI. Key challenges include data scarcity in high-performance materials and the need for physics-informed UQ to mitigate overconfidence in extrapolative predictions. This synthesis underscores the importance of robust uncertainty handling for responsible AI deployment in materials innovation.

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.

This review systematically examines conceptual approaches to uncertainty communication in materials artificial intelligence, synthesizing insights from 35 peer-reviewed publications published between 2017 and 2025 that span uncertainty quantification techniques, visualization strategies, human-factors research, and domain-specific applications in computational materials science. The methodology involved targeted searches across Web of Science, Scopus, arXiv, and PubMed using strings such as “uncertainty communication” machine learning, “uncertainty visualization” materials AI, “predictive uncertainty” materials informatics, and related terms, with strict inclusion criteria limited to English-language peer-reviewed works that explicitly address the reporting, visualization, or human interpretation of uncertainty estimates, yielding a final corpus of 35 core references after PRISMA-style screening. Foundations of uncertainty communication are drawn from risk-communication literature and cognitive science, emphasizing that effective transmission of predictive uncertainty is essential for building trust and enabling sound decision-making. Yet, it remains distinct from mere quantification because users frequently misinterpret or ignore numerical confidence measures when they lack contextual framing. Current practices in materials AI reveal a persistent gap: while uncertainty quantification is increasingly present through confidence intervals or ensemble variances, explicit communication to end users—whether fellow researchers or industrial decision-makers—is rare, often limited to parenthetical standard deviations or simple error bars that fail to convey epistemic versus aleatoric components or their implications for downstream materials design. Approaches to uncertainty communication surveyed here encompass numerical, visual, verbal, interactive, and decision-focused modalities, each evaluated for strengths and limitations when applied to high-stakes materials predictions. Materials-specific challenges, including multi-scale propagation and costly experimental validation, exacerbate these issues, leading to identified gaps such as the absence of standardized reporting guidelines and limited empirical studies on user understanding; the review concludes with actionable recommendations for authors, journals, reviewers, and the broader community to elevate uncertainty communication from an afterthought to a core pillar of responsible materials AI.

Computational materials engineering has undergone a transformative shift with the integration of data-driven methodologies and artificial intelligence, enabling accelerated discovery and design of novel materials. Uncertainty quantification (UQ) plays a pivotal role in this paradigm, addressing inherent variabilities in simulations, experimental data, and model predictions to ensure reliable decision-making in materials development. This review synthesizes recent advancements in UQ methods within computational and data-driven materials engineering, focusing on probabilistic modeling, sensitivity analysis, and Bayesian inference techniques deployed across multiscale simulations and machine learning frameworks. We examine deployment contexts ranging from molecular dynamics to additive manufacturing, highlighting how UQ enhances robustness in property prediction, process optimization, and autonomous discovery systems. By integrating insights from high-impact studies the review delineates a systems-level perspective on UQ infrastructures, emphasizing their role in bridging computational predictions with experimental validation. Key challenges such as computational efficiency and data scarcity are contextualized, alongside opportunities for multimodal integration. Ultimately, this synthesis positions UQ as an essential infrastructure for advancing materials informatics toward industrial applicability, offering a forward-looking outlook on scalable, uncertainty-aware workflows in materials engineering.