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The integration of artificial intelligence into materials science has accelerated discovery processes, yet the persistent challenge of data scarcity undermines the full potential of these technologies. This conceptual paper develops a novel theoretical framework for understanding small-data regimes in materials AI, emphasizing the interpretive dynamics that emerge when limited datasets intersect with domain knowledge and computational strategies. By synthesizing recent literature, the framework explains how scarcity influences model behavior through mechanisms of uncertainty amplification and knowledge integration, revealing interaction patterns between sparse empirical inputs and physics-informed priors. Analytical implications include enhanced epistemic reasoning about model reliability in low-data contexts, where trade-offs between generalization and specificity manifest in feedback structures that guide iterative refinement. Conceptual interpretations highlight steering logics that balance data-driven insights with theoretical constraints, fostering systems-level insights into how small-data environments reshape AI workflows in materials design. The framework underscores ethical considerations in deploying such systems, particularly regarding bias propagation under scarcity. Through a detailed textual description of a schematic figure, the paper illustrates these dynamics and offers integrative perspectives for advancing materials informatics without relying on large-scale data collection. Ultimately, this theory reorients focus toward resilient AI architectures that thrive amid informational constraints, promoting sustainable innovation in the field.

Materials artificial intelligence (MAI) has revolutionized the discovery, design, and optimization of new materials by leveraging machine learning algorithms to analyze complex datasets and predict properties with high accuracy. However, the rapid proliferation of MAI tools has raised critical questions about benchmarking practices, which are essential for evaluating model performance, ensuring reproducibility, and addressing ethical concerns. This narrative review examines current benchmarking frameworks in MAI, highlighting what is effectively measured—such as predictive accuracy and computational efficiency—and what is often overlooked —such as data bias, interpretability, fairness, and ethical implications. Drawing on recent advances in frameworks such as JARVIS-Leaderboard and Matbench, the review discusses challenges in data quality, reproducibility, and the integration of explainable AI (XAI) methods. It also explores active learning strategies for optimizing materials discovery under limited data conditions and proposes directions for more inclusive and transparent benchmarking. By synthesizing insights from diverse studies, this review aims to guide future MAI research toward robust, equitable, and ethically sound practices that accelerate innovation while mitigating risks.

Uncertainty quantification (UQ) has become a routine component of materials artificial intelligence (AI), with predictive models now systematically reporting confidence intervals or variance estimates alongside outputs spanning formation energies to mechanical properties. Despite this integration, the designation “trustworthy uncertainty” remains conceptually unresolved. Assertions of reliability are frequently decoupled from operational criteria that connect statistical behavior to the concrete decisions faced by materials scientists, including large-scale screening, experimental prioritization, design optimization, certification under regulatory constraints, and the interpretation of anomalous phenomena. Addressing this gap, the present boundary-focused analysis advances a definition of trustworthiness grounded in decision relevance and introduces the construct of decision-ready confidence. This formulation identifies a set of boundary conditions that collectively determine whether uncertainty estimates can support action. Calibration ensures correspondence between predicted uncertainty and empirical error distributions, while sharpness constrains interval width to maintain discriminative value without sacrificing validity. A related requirement concerns the separation of epistemic and aleatoric components, enabling differentiation between reducible and irreducible uncertainty. Coverage, particularly in its conditional form, establishes reliability at the level of individual predictions, and stability enforces robustness under small perturbations of input space. These statistical conditions are complemented by computational tractability, which situates uncertainty estimation within the temporal constraints of decision-making processes. Crucially, none of these properties is intrinsic in isolation; each must be interpreted relative to the decision context in which the model is deployed. To anchor these criteria, the analysis delineates a set of recurring decision regimes that structure materials AI workflows, spanning screening, experimental validation, active learning, optimization, certification, and discovery. Each regime imposes distinct requirements on uncertainty behavior, thereby redefining trustworthiness as a context-dependent alignment rather than a universal attribute. Building on this premise, a framework for decision-ready confidence is introduced to formalize the mapping between decision type, required UQ properties, validation procedures, risk thresholds, and reporting practices. This framework integrates and extends established approaches, including epistemic–aleatoric decomposition, conformal prediction, and broader trustworthy machine-learning paradigms, while situating them within the operational realities of materials engineering. In doing so, it establishes a coherent conceptual foundation for evaluating and deploying UQ methods whose outputs are not only statistically sound but also directly actionable in advancing materials discovery and ensuring system-level reliability.