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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.

Machine learning interatomic potentials have become central tools in computational materials engineering, promising accurate and scalable predictions of alloy properties. Benchmarks in the field routinely report impressive accuracies, such as energy mean absolute errors below 20 meV/atom and low force root-mean-square errors, leading many papers to conclude that their models generalize well to alloy systems. Yet when these same potentials are deployed on real-world disordered alloys—such as binary solid solutions or multi-principal-element high-entropy alloys—the accuracy collapses, often by an order of magnitude. The root cause lies in the benchmarking pipeline itself: training and testing occur almost exclusively on ordered crystal structures drawn from databases of intermetallic compounds, while disordered configurations, which dominate practical applications, are never evaluated. This critical critique identifies four primary sources of systematic overestimation. First, the near-universal reliance on ordered training and test sets creates an illusion of generalization that fails the moment configurational disorder is introduced. Second, random or composition-based splits on ordered data allow models to memorize recurring structural motifs rather than learn transferable physics. Third, even when special quasirandom structures are employed as proxies for disorder, their periodic nature and limited sampling of local environments produce a misleading “SQS mirage.” Fourth, energy-only metrics mask severe force-prediction failures that render molecular-dynamics simulations unstable in disordered systems. These benchmarking flaws have concrete consequences: overconfidence among practitioners, misallocation of experimental resources, and slowed progress in high-entropy alloy design. By drawing on the literature, this article demonstrates that the ordered-to-disordered gap is not an edge case but a fundamental and under-reported limitation. It proposes concrete reforms—mandatory disordered test sets, configurational sampling protocols, and separate reporting of ordered versus disordered performance—to restore credibility to ML potential benchmarks. Until these changes are adopted, claims of “alloy-ready” potentials should be treated with skepticism. The field must move beyond ordered-crystal comfort zones if machine-learned potentials are to deliver on their promise for the compositionally complex materials that define modern materials science.

High-entropy alloys (HEAs) and multi-principal element alloys (MPEAs) occupy an enormous compositional space that conventional computational approaches cannot fully explore. A five-element system with 10 % concentration steps already contains millions of distinct nominal compositions, each further multiplied by exponentially many atomic configurations arising from configurational disorder. Machine learning (ML) interatomic potentials have been proposed as a scalable solution to accelerate property prediction and materials design in this vast space. Yet the compositional generalizability of these potentials—their capacity to make reliable predictions for compositions and local environments lying outside the training distribution—remains largely unexamined in a systematic way. Most existing benchmarks focus on interpolation within narrow ranges of ordered or equiatomic compounds, leaving critical extrapolation scenarios untested. This conceptual framework article identifies four distinct but interrelated dimensions of compositional generalizability in HEA ML potentials: (i) element extrapolation, (ii) concentration interpolation, (iii) multi-element recombination, and (iv) local environment diversity. It proposes a four-component assessment framework—training-set characterization, test-set design, dimension-specific generalization metrics, and a structured validation protocol—that enables researchers to quantify generalization gaps without relying on performance numbers or simulation results. For each dimension, explicit validation strategies and success criteria are defined, grounded in the literature on special quasirandom structures, graph-network representations, and equivariant architectures. The framework is deliberately conceptual, emphasizing definitions, relationships among components, assessment criteria, and diagnostic reasoning rather than empirical data. By adopting this framework, the community can move beyond ad-hoc testing and develop ML potentials that truly generalize across the high-dimensional alloy landscape. The implications extend to more reliable high-throughput screening, accelerated discovery of novel HEAs, and clearer guidance for training-set design and model architecture choices. Ultimately, systematic assessment of compositional generalizability will help ensure that ML potentials fulfill their promise for complex concentrated alloys.

The reliability of machine-learned (ML) interatomic potentials in phase space sampling depends critically on whether sampled configurations lie within the interpolative domain of the training data or extend into extrapolative regimes. Despite its central importance, the interpolation–extrapolation distinction remains inconsistently defined across the literature. Existing single-metric approaches—such as convex-hull composition checks, descriptor-space distances, uncertainty estimates, and local-environment similarity—capture only partial aspects of high-dimensional configuration space and frequently yield conflicting classifications. This lack of a rigorous, unified boundary undermines active learning strategies, uncertainty quantification, benchmarking, and the safe deployment of ML potentials in safety-critical applications. This boundary/definitional article introduces a unified and operational framework that delineates the interpolation–extrapolation boundary through four independent and jointly necessary criteria: (C1) compositional coverage, (C2) local-environment similarity, (C3) configurational co-occurrence, and (C4) thermodynamic condition. A configuration is defined as interpolative only when all four criteria are simultaneously satisfied; violation of any criterion constitutes extrapolation. To replace binary classification, the framework further introduces a graded hierarchy of extrapolation—mild, moderate, and severe—based on the number and magnitude of violations. The proposed definitions are deliberately operational, relying exclusively on information available during training-set construction or simulation runtime, and are agnostic to model architecture. Their adoption enables standardized reporting, stratified benchmarking, improved calibration of uncertainty estimates, and more targeted active-learning workflows. By establishing a clear and reproducible boundary, the framework provides a principled foundation for evaluating model reliability during phase space exploration. This work advances the epistemic rigor of ML-driven materials modeling and supports the responsible and transparent deployment of data-driven interatomic potentials.

Machine learning interatomic potentials now enable molecular dynamics simulations with near-density-functional-theory accuracy at scales inaccessible to conventional quantum methods. Yet most remain fundamentally static: once trained, they are deployed without adaptation, even as simulations enter configurations outside the original training distribution. Although self-consistency ensures that predicted forces remain exact derivatives of the learned energy surface, it guarantees only internal coherence, not fidelity to the true reference landscape. As a result, small local errors can accumulate into substantial long-timescale drift. This paper proposes a conceptual framework for error-correcting machine learning potentials based on on-the-fly residual learning. The architecture combines a self-consistent base predictor with an error detector, a lightweight residual corrector, an online updater, and a memory manager. Embedded directly within the molecular dynamics loop, these components enable the system to identify unreliable predictions, apply immediate corrections, selectively request sparse density-functional-theory labels, and retain corrective knowledge during continuous adaptation. By shifting from static deployment to simulation-aware error correction, the framework addresses the central limitations of extrapolation failure and accumulated drift. It therefore outlines a path toward adaptive machine learning potentials capable of sustaining reliable long-timescale materials simulations with controlled computational overhead.

Machine learning interatomic potentials achieve high accuracy at molecular-dynamics scales but rely on local descriptors that truncate long-range interactions. In charge-transfer-sensitive materials, where electrostatics decay as 1/r, this assumption fails. The resulting mismatch produces systematic errors, including non-convergent energies, force discontinuities, unstable charge distributions, oxidation-state ambiguity, suppressed ionic transport, and collapsed dielectric response. These effects are not incidental but arise directly from the absence of global electrostatic coupling. This work presents a failure-mode analysis that links these breakdowns to their physical origin and introduces practical diagnostics based on standard simulation outputs. It further outlines mitigation strategies that restore long-range physics through electrostatic solvers, charge equilibration, multipole representations, and hybrid architectures. By establishing when and why locality fails, the study defines the minimal requirements for charge-transfer-aware machine learning potentials and provides a pathway toward reliable simulation of ionic and polar materials.

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.