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Uncertainty quantification in machine learning (ML) interatomic potentials remains fundamentally limited by the conflation of epistemic uncertainty, arising from incomplete sampling of configuration space, and aleatoric uncertainty, embedded in reference data generated by density-functional theory. Existing approaches provide internally consistent uncertainty estimates but collapse these distinct sources into a single scalar, obscuring the mechanisms governing model reliability and limiting principled decision-making. This work introduces a modular, architecture-agnostic framework that enforces explicit separation of epistemic and aleatoric contributions at the level of model design rather than post hoc analysis. The framework defines five interoperable components—a shared feature extractor, dedicated epistemic and aleatoric modules, an aggregation mechanism, and a calibration stage—whose interactions preserve disentanglement throughout training, inference, and downstream application. The resulting formulation transforms uncertainty into an operational diagnostic. Epistemic uncertainty identifies regions where additional data acquisition is informative, whereas aleatoric uncertainty defines the intrinsic accuracy ceiling imposed by the reference method. This separation restructures active learning by directing sampling toward reducible error, enables meaningful comparison between models through their uncertainty composition, and grounds performance evaluation relative to an explicit noise floor. The framework further introduces operational criteria that provide falsifiable tests of successful separation, ensuring that reported uncertainties remain interpretable and consistent across architectures. By decoupling learnable structure from irreducible variability, the proposed approach establishes a principled foundation for uncertainty-aware ML potentials, supporting more efficient data allocation, more reliable atomistic simulations, and more rigorous standards for model development in computational materials science.

High-risk materials predictions in fields such as nuclear reactor design, aerospace component qualification, and advanced energy storage systems demand not only accurate property forecasts but also uncertainty estimates that can be trusted under regulatory scrutiny. Materials graph neural networks have emerged as powerful tools for property prediction across composition and structure spaces, yet the overwhelming majority output a single scalar uncertainty that conflates two fundamentally different sources: epistemic uncertainty arising from incomplete or sparse training data and aleatoric uncertainty arising from irreducible noise in the underlying data-generation process itself. This conceptual framework paper demonstrates that failure to separate these uncertainty types leads to flawed risk assessments and inefficient decision-making. Epistemic uncertainty signals the need for additional data collection or targeted active learning, whereas aleatoric uncertainty signals the need for higher-fidelity data sources such as improved density-functional approximations or experimental validation. The proposed modular framework equips materials graph networks with dedicated estimators for each uncertainty type, an aggregator that applies risk-adjusted safety margins, a decision gate that triggers appropriate remediation actions, and a calibration certificate required for deployment in safety-critical contexts. Operational criteria are defined to validate the separation in practice, including data-scaling behavior, source-identification tests, and active-learning efficiency. By enabling precise mapping of uncertainty to actionable protocols, the framework bridges a critical gap between current uncertainty-quantification practices in graph neural networks and the stringent reliability requirements of high-risk materials engineering. It builds directly on foundational distinctions introduced for deep learning while adapting them to the unique challenges of crystal-graph representations and multi-fidelity materials data. The result is a conceptual blueprint that supports safer model deployment, more efficient discovery campaigns, and clearer pathways to regulatory approval.