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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.

Physics-guided machine learning (PGML) has emerged as a hybrid paradigm in materials science, integrating domain knowledge with data-driven methods to enhance predictive accuracy and generalizability. Conventional approaches typically embed physical principles as soft inputs—either through loss-function regularization or auxiliary features—allowing violations during optimization. This manuscript advances a conceptual reframing in which physics operates as a hard constraint on the model’s hypothesis space rather than as an additive input. By restricting permissible functional forms, symmetries, and conservation relations a priori, the framework enforces physical consistency at the architectural level, altering the interaction dynamics between data and prior knowledge. The reframing yields systems-level insights into epistemic trade-offs: reduced reliance on large datasets, improved extrapolation beyond training regimes, and inherent satisfaction of thermodynamic or mechanical invariants critical to materials behavior. Analytical implications include feedback structures that couple data refinement to constraint satisfaction, revealing emergent robustness in multiscale modeling. This perspective addresses persistent challenges in materials science, such as sparse experimental data and complex microstructure-property relationships, without resorting to empirical validation. The contribution lies in reinterpreting PGML’s epistemic foundation, steering future developments toward constraint-centric designs that prioritize physical fidelity over post-hoc penalization.

The integration of artificial intelligence (AI) into materials science represents a paradigm shift in how scientific creativity is manifested and harnessed. This conceptual paper develops a novel theoretical framework for understanding AI-mediated hypothesis generation, emphasizing its role in enhancing scientific creativity within materials discovery and design. Traditional hypothesis generation in materials science relies on human intuition, empirical observation, and theoretical deduction, often constrained by cognitive limitations and the vast complexity of material systems. AI, through machine learning algorithms and generative models, augments this process by enabling rapid pattern recognition, simulation of hypothetical scenarios, and exploration of uncharted chemical spaces. The proposed framework, termed the symbiotic creativity cycle (SCC), posits a dynamic interplay between human and AI agents, where AI serves as a cognitive amplifier, facilitating divergent exploration and convergent refinement of hypotheses. This cycle incorporates iterative feedback loops that integrate domain knowledge with data-driven insights, fostering emergent creativity that transcends individual capabilities. Key elements includeAI’s ability to handle multidimensional data, predict material properties, and generate novel conceptual blends. The framework highlights potential applications for accelerating discoveries in advanced alloys, nanomaterials, and energy storage materials, while addressing challenges such as interpretability and ethical integration. By reconceptualizing scientific creativity as a hybrid human-AI endeavor, this paper lays the foundation for future theoretical developments and practical applications in applied artificial intelligence for materials science. Ultimately, AI-mediated hypothesis generation promises to democratize innovation, enabling more efficient navigation of the materials design landscape.

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.