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The integration of surrogate modeling with high-throughput density functional theory (DFT) calculations has transformed materials discovery by enabling rapid screening of vast chemical spaces to predict properties. However, the inherent uncertainties in both DFT computations and surrogate approximations provide conceptual challenges to the reliability of screening results. This paper offers a conceptual reinterpretation of uncertainty in the screening of surrogate-driven materials and emphasizes how uncertainty reshapes the logic of discovery processes. We synthesize recent literature to highlight tensions between computational efficiency and predictive fidelity, where surrogate models approximate DFT data but introduce epistemic uncertainties from model simplifications and aleatory uncertainties from stochastic elements in ab initio simulations. By reframing uncertainty not merely as an error to minimize but as an informative signal guiding decision confidence, we argue for a paradigm in which uncertainty informs adaptive screening strategies, altering discovery trajectories toward more robust material identifications. This conceptual change emphasizes the need to integrate awareness of uncertainty into interpretive structures, fostering a nuanced understanding of how uncertainties propagate through screening paradigms. Ultimately, this perspective invites a critical examination of uncertainty’s role in bridging AI and DFT, promoting theoretical integration that enhances the interpretability and trustworthiness of AI-assisted materials discovery without relying on prescriptive frameworks.

Artificial intelligence (AI) is increasingly embedded across the materials design lifecycle. Yet, prevailing approaches to trustworthiness remain largely model-centric, emphasizing predictive accuracy while under-specifying how AI outputs translate into high-stakes material decisions. This limitation is particularly consequential in materials science, where decisions frequently commit resources to irreversible synthesis, deployment, and long-term societal or environmental impact. Here, we propose a novel decision-centric conceptual framework for trustworthy AI in materials design, defining trustworthiness as the justification of action recommendations under uncertainty—including decisions to select, reject, prioritize, stop, or redesign candidate materials—rather than as an intrinsic property of models alone. The framework structures the materials lifecycle as an iterative sequence of seven decision-bearing stages—from problem framing to revision—and introduces five validity gates—scope, domain, uncertainty, consequence, and sustainability—that serve as systematic filters between AI outputs and actionable commitments. Trust dimensions such as reliability, robustness, transparency, accountability, safety, and sustainability are conceptualized as emergent properties of gated lifecycle interactions rather than isolated criteria. By identifying where failures originate across the lifecycle and formalizing named failure modes with corresponding containment principles, the framework explicitly links uncertainty quantification, interpretability, and governance considerations to defensible decision-making in materials contexts. This work provides a unifying theoretical structure for understanding how trustworthy AI decisions can be operationalized in materials design, offering conceptual grounding for future methodological, institutional, and governance advances in applied artificial intelligence for materials science.

In the domain of applied artificial intelligence (AI) for materials science, uncertainty emerges as a pivotal signal that informs design decisions, yet its conceptual interpretation remains underexplored. This paper delineates uncertainty as the lack of complete knowledge about a system's state or outcomes, distinct from confidence, which reflects a model's self-assessed reliability in predictions; risk, which weights uncertainty by potential consequences; and actionability, which denotes the warrant for proceeding with design actions based on interpreted signals. Traditional approaches often conflate these concepts, leading to suboptimal decisions in materials discovery and optimization. For instance, high confidence in AI predictions may not equate to low risk in high-stakes applications like alloy design for extreme environments, where epistemic gaps could amplify failures. This conceptual manuscript proposes a decision-theoretic framework, the Uncertainty-to-Action Map, that translates uncertainty types—epistemic, aleatory, and semantic—into risk postures and subsequent action classes, such as screening candidates, prioritizing explorations, deferring judgments, redesigning models, stopping pursuits, hedging bets, or diversifying portfolios. By incorporating gates for stake assessment, ambiguity detection, domain scope evaluation, cost asymmetry analysis, and stopping logic, the framework mitigates failure modes like overconfidence and decision paralysis. This model fosters a nuanced view, emphasizing that uncertainty, when properly interpreted, serves as a design asset rather than a hindrance, promoting robust AI-assisted materials innovation.

Human-in-the-loop (HITL) approaches are increasingly invoked in materials artificial intelligence (AI) as a presumed remedy for unreliable models, opaque predictions, and domain-shift failures. Yet “including a human” often functions as a rhetorical assurance rather than a precise scientific claim, masking the fact that humans participate in materially different ways: as labelers, judges, curators, constraint designers, hypothesis framers, risk owners, and accountability anchors. This conceptual manuscript argues that HITL is not a single method but a family of epistemic and governance roles that shape what an AI output means, what it can justify, and what actions it can responsibly warrant. Building on recent developments in materials informatics, active learning, uncertainty quantification, interpretable machine learning, and scientific machine learning, we synthesize a theory-first view of human involvement as a structured intervention in the AI-to-decision pathway rather than an informal override mechanism. We introduce a novel taxonomy that distinguishes (i) where humans intervene in the pipeline (data, representation, model, evaluation, decision), (ii) what kind of authority they exert (epistemic, normative, operational), and (iii) how their involvement changes the legitimacy of downstream claims under differing stakes. The resulting framework replaces HITL hype with a falsifiable conceptual vocabulary for designing responsibility, reliability, and restraint in materials AI.

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 exploration faces persistent challenges stemming from vast chemical spaces, high experimental costs, and inherent uncertainties in predictive models. While machine learning has accelerated property prediction and guided candidate selection, conventional approaches often treat uncertainty as a uniform metric within fixed acquisition strategies. This conceptual paper introduces uncertainty-conditioned experiment planning (UCEP) as a novel theoretical framework for AI-guided materials discovery. UCEP reframes experiment planning as a dynamic process conditioned on the multidimensional character of uncertainty, integrating epistemic and aleatoric components, data-related biases, and model limitations into the steering logic. Rather than relying on static acquisition functions, the framework emphasizes adaptive interaction dynamics between uncertainty characterization and planning decisions, enabling context-sensitive trade-offs between exploration, exploitation, and bias mitigation. Drawing on interpretive insights from materials informatics and uncertainty quantification literature, UCEP highlights systems-level feedback structures that can enhance epistemic robustness and scientific efficiency without presupposing empirical outcomes. The framework offers analytical implications for rethinking how AI systems interpret and respond to uncertainty in iterative discovery cycles, contributing to more reflective and integrative AI-assisted materials research.

The integration of artificial intelligence (AI) and machine learning (ML) in materials science has accelerated discovery and design processes, yet it introduces challenges related to failure, uncertainty, and risk. This narrative review examines how the materials AI literature addresses negative outcomes, including model uncertainties, predictive failures, and associated risks in application. Drawing on peer-reviewed studies, we explore uncertainty quantification techniques, robustness evaluations, and risk mitigation strategies. Key themes include Bayesian methods for uncertainty estimation, benchmark studies on prediction reliability, and strategies to handle data scarcity and extrapolation errors. The review highlights gaps in handling adversarial conditions and real-world failures, proposing future directions for more resilient AI frameworks in materials research. By synthesizing these insights, we aim to foster a more cautious and effective use of AI in advancing materials innovation.

This review systematically examines conceptual approaches to uncertainty communication in materials artificial intelligence, synthesizing insights from 35 peer-reviewed publications published between 2017 and 2025 that span uncertainty quantification techniques, visualization strategies, human-factors research, and domain-specific applications in computational materials science. The methodology involved targeted searches across Web of Science, Scopus, arXiv, and PubMed using strings such as “uncertainty communication” machine learning, “uncertainty visualization” materials AI, “predictive uncertainty” materials informatics, and related terms, with strict inclusion criteria limited to English-language peer-reviewed works that explicitly address the reporting, visualization, or human interpretation of uncertainty estimates, yielding a final corpus of 35 core references after PRISMA-style screening. Foundations of uncertainty communication are drawn from risk-communication literature and cognitive science, emphasizing that effective transmission of predictive uncertainty is essential for building trust and enabling sound decision-making. Yet, it remains distinct from mere quantification because users frequently misinterpret or ignore numerical confidence measures when they lack contextual framing. Current practices in materials AI reveal a persistent gap: while uncertainty quantification is increasingly present through confidence intervals or ensemble variances, explicit communication to end users—whether fellow researchers or industrial decision-makers—is rare, often limited to parenthetical standard deviations or simple error bars that fail to convey epistemic versus aleatoric components or their implications for downstream materials design. Approaches to uncertainty communication surveyed here encompass numerical, visual, verbal, interactive, and decision-focused modalities, each evaluated for strengths and limitations when applied to high-stakes materials predictions. Materials-specific challenges, including multi-scale propagation and costly experimental validation, exacerbate these issues, leading to identified gaps such as the absence of standardized reporting guidelines and limited empirical studies on user understanding; the review concludes with actionable recommendations for authors, journals, reviewers, and the broader community to elevate uncertainty communication from an afterthought to a core pillar of responsible materials AI.

Uncertainty quantification (UQ) has become indispensable for the trustworthy deployment of machine learning interatomic potentials (MLIPs) in materials science and molecular modeling, where predictions of energies, forces, and derived properties directly inform high-stakes decisions in materials discovery, long-time-scale molecular dynamics, and autonomous design workflows. Without reliable uncertainty estimates, MLIPs risk propagating errors that compromise simulation stability, mislead experimental prioritization, or produce unphysical results in extrapolation regimes critical to novel alloy or molecular discovery. This review synthesizes the literature on UQ methods specifically developed for or applied to MLIPs, drawing exclusively from the compiled reference set to provide a focused, critical overview of progress during this formative period. The scope is deliberately restricted to UQ techniques for interatomic potentials themselves (including GAP, DeepMD, ANI-series, SchNet-derived, and E(3)-equivariant models such as NequIP), excluding standalone ML property prediction unless the method directly supports force-field uncertainty. A systematic taxonomy organizes existing approaches into five methodological families—Bayesian and probabilistic methods, ensemble methods, Gaussian process and kernel methods, conformal prediction and frequentist methods, and heuristic and ad hoc methods—highlighting their distinct mathematical foundations and practical implementations in MLIP contexts. Hidden assumptions pervading these families are identified and dissected, including independence of atomic errors, Gaussianity of predictive distributions, homoscedasticity across chemical space, kernel-imposed smoothness in Gaussian processes, approximation quality in variational or Monte-Carlo inference, and exchangeability in conformal frameworks. These assumptions frequently remain unstated yet profoundly influence calibration and reliability when MLIPs are deployed in production simulations. Unresolved questions are articulated with precision: how to treat correlated uncertainties along molecular-dynamics trajectories, the absence of a true ground-truth uncertainty given DFT approximations, the prohibitive computational overhead of scalable UQ, evaluation under distribution shift, vectorial uncertainty for forces rather than scalar energies, detection of physical inconsistencies, and hierarchical fusion of model, data, and ab-initio uncertainties. Future outlook points toward integrated UQ-driven active learning, force-aware uncertainty representations, and hybrid methods that balance calibration, sharpness, and efficiency for next-generation autonomous materials engineering.

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.

Active learning is widely used in materials discovery to reduce the number of expensive density functional theory calculations required for surrogate-model training. Its dominant acquisition rule, uncertainty sampling, assumes that the most uncertain configuration will also yield the greatest information gain. Although this logic performs well for many bulk properties, it breaks down for defect formation energies. In this setting, uncertainty sampling repeatedly selects uninformative structures, misallocates computational budget, and fails to reach the defective configurations that govern vacancy, interstitial, and substitutional behavior in technologically important materials. This failure reflects a structural mismatch between the acquisition rule and defect physics. Defect configurations are rare in configuration space, energetics are highly localized, and DFT labels often contain substantial aleatoric noise from finite-size effects, charge corrections, and supercell artifacts. These conditions weaken the link between predictive uncertainty and useful learning signal, while representation bias in models trained mainly on perfect crystals further erodes selectivity. This study develops a failure-mode analysis of uncertainty sampling for defect formation energies, identifying recurrent breakdowns in sampling, representation, calibration, and budget use. It also outlines practical detection principles and defect-aware mitigation strategies, including initialization with defective structures, hybrid acquisition functions, epistemic-only scoring, and budget partitioning. The central implication is that active learning for defect discovery cannot rely on generic uncertainty-based querying. Effective acceleration in this domain requires acquisition strategies designed around the rarity, locality, and noise structure of defect energetics.

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.

Ensemble methods have become the dominant framework for uncertainty quantification in machine learning models for high-entropy alloy (HEA) property prediction, where variance across independently trained neural networks is routinely interpreted as epistemic uncertainty. This metric now underpins active learning, compositional screening, and experimental decision-making, largely due to its simplicity and success in data-rich domains. This work shows that such reliance is fundamentally misplaced in HEAs. Ensemble variance implicitly assumes IID sampling, feature-space isotropy, uniform error, independence among models, and Gaussian residuals—conditions that are systematically violated in compositionally complex alloys. HEA datasets are biased toward equiatomic, stable compositions, the compositional manifold is anisotropic, predictive error is strongly heteroscedastic, ensemble members exhibit correlated failures, and extrapolation induces heavy-tailed errors. Under these conditions, ensemble variance becomes miscalibrated, underestimating uncertainty in sparse regions while overstating model reliability. The resulting distortions propagate through discovery workflows, yielding inefficient active-learning strategies, overconfident extrapolation, and misleading experimental guidance, even as larger ensembles appear to reduce uncertainty without improving accuracy. By linking these failures to their statistical origins, this paper clarifies why standard ensemble diversity is not a faithful proxy for epistemic uncertainty in HEAs. It further outlines diagnostics and targeted corrections that align uncertainty estimates with the physics and data structure of complex alloy systems, enabling more reliable and efficient materials discovery.

Compositional design spaces in materials engineering, particularly for alloys and multi-component systems, present exceptionally high-dimensional and sparsely populated landscapes that challenge conventional discovery workflows. A typical five-element alloy system sampled at 10% concentration increments can encompass millions of possible compositions, rendering exhaustive evaluation infeasible. Active learning has emerged as an essential paradigm for navigating these spaces efficiently, yet standard implementations often fail to address the simultaneous demands of multiple conflicting objectives—such as balancing mechanical strength against ductility, electrical conductivity against thermal stability, or performance against material cost—while properly accounting for uncertainty and practical constraints. This conceptual framework introduces an uncertainty-aware active learning approach tailored specifically for compositional design spaces with multiple objectives. The framework comprises four core components: robust uncertainty quantification that distinguishes epistemic from aleatoric contributions across heterogeneous composition regions, multi-objective acquisition functions grounded in Pareto optimization to explicitly explore trade-offs, systematic constraint handling for both hard thermodynamic limits and soft economic or manufacturability requirements, and adaptive sequential sampling strategies that prioritize informative candidates under limited evaluation budgets. The key innovation lies in a Pareto-based acquisition mechanism that integrates uncertainty estimates to balance exploration of uncertain regions, exploitation of promising trade-offs, and navigation of sparse high-dimensional manifolds. By embedding these elements into a unified conceptual structure, the framework provides design principles that enable more reliable, interpretable, and scalable materials discovery. It shifts the focus from single-objective optimization to holistic decision-making under uncertainty, ensuring that active learning not only accelerates identification of high-performing compositions but also respects real-world engineering constraints inherent to multi-component systems. The proposed approach is expected to guide both computational and experimental campaigns in alloy design, high-entropy materials, and mixed ionic conductors, ultimately fostering more sustainable and high-performance 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.

The field of computational and data-driven materials engineering has transformed traditional discovery processes through the integration of machine learning, high-throughput computations, and autonomous systems. However, as these pipelines scale, the management of uncertainty emerges as a foundational infrastructure rather than a mere analytical byproduct. This manuscript conceptualizes uncertainty not as an obstacle but as an enabling framework for governing confidence in materials informatics workflows. By synthesizing recent advancements in representation learning, graph neural networks, and uncertainty quantification, we identify epistemic gaps in current data-driven ecosystems, where confidence in predictions often remains opaque or inadequately integrated into discovery loops. We introduce the Confidence Governance Framework (CGF), a layered conceptual architecture that embeds uncertainty quantification as a core infrastructural element, facilitating dynamic interactions between data representations, model inferences, and discovery steering. This framework emphasizes computational trade-offs in multimodal datasets and simulation-experiment couplings, promoting robust, interpretable pipelines. Implications extend to enhanced autonomy in inverse design and closed-loop experimentation, fostering resilient materials engineering paradigms. Through this lens, uncertainty becomes a strategic asset for calibrating epistemic risks and optimizing resource allocation in AI-assisted materials research.