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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.

Materials informatics has emerged as a central paradigm in contemporary materials science, leveraging machine learning and data-driven modeling to accelerate materials discovery, optimization, and deployment. Despite substantial advances in predictive accuracy, most existing approaches remain fundamentally correlational, limiting their reliability under distribution shifts, experimental interventions, and real-world deployment scenarios. This reliance on correlation constrains scientific interpretability and undermines the capacity of AI systems to function as genuine instruments of materials reasoning. Causality offers a principled framework for overcoming these limitations by explicitly modeling cause-and-effect relationships among composition, processing, structure, and properties. This narrative review synthesizes conceptual progress in integrating causal inference into materials informatics, examining foundational causal frameworks, advances in causal discovery, and hybrid causal–machine learning approaches, and emerging applications across materials domains such as nanocatalysis, ferroelectrics, and electrochemical energy storage. We critically analyze persistent challenges—including data scarcity, assumption violations, limited external validity, and computational and epistemic constraints—that currently hinder widespread adoption. Drawing exclusively on peer-reviewed literature published, the review emphasizes thematic and epistemic developments rather than algorithmic prescriptions. We argue that causality represents a structural shift in how AI systems contribute to materials science: from correlational predictors to intervention-aware, mechanism-aligned reasoning tools. By articulating future directions centered on hybrid modeling, domain-knowledge integration, and interdisciplinary collaboration, this review positions causality as a necessary foundation for robust, generalizable, and scientifically legitimate materials informatics.

The rapid evolution of computational and data-driven materials engineering has ushered in an era where self-driving laboratories (SDLs) promise to transform materials discovery by integrating automation, machine learning, and high-throughput experimentation into cohesive governance architectures. These architectures orchestrate the interplay between data generation, model training, and decision-making processes to enable closed-loop optimization in materials design. This review synthesizes recent advancements in SDL governance, focusing on how computational workflows—encompassing materials informatics, graph neural networks, representation learning, and uncertainty quantification—facilitate autonomous systems in addressing complex materials challenges. We examine the foundational elements of data-driven ecosystems, including multimodal datasets and simulation-experiment integration, and explore active learning strategies that balance exploration and exploitation in inverse design paradigms. Key governance components, such as orchestration platforms like ChemOS 2.0 and Bayesian active learning frameworks, are analyzed for their role in accelerating discovery cycles. By integrating perspectives from high-impact studies, we highlight how these architectures mitigate inefficiencies in traditional trial-and-error approaches, enabling scalable, reproducible materials innovation. The review positions SDL governance as a critical infrastructure for future materials engineering, emphasizing systems-level integration over isolated techniques. Ultimately, it underscores the potential of these architectures to democratize access to advanced materials development while identifying pathways for enhanced interoperability and robustness in computational ecosystems.