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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 accelerating integration of artificial intelligence into materials selection processes has brought unprecedented efficiency to high-throughput screening and discovery campaigns, yet it has also introduced a subtle but profound failure mode that remains largely unrecognized in the field: scientific regret. This paper identifies scientific regret as a distinct failure mode in AI-driven materials science—the ex-post realization that a better material or research direction was passed over due to an AI recommendation, often under conditions of irreducible uncertainty and vast combinatorial search spaces. Unlike traditional statistical errors, scientific regret captures the experiential and consequential dimension of missed opportunities in research trajectories that are difficult or impossible to reverse. Drawing on foundational work in decision theory and recent advances in Bayesian optimization for materials discovery, the paper defines scientific regret, delineates its mechanisms of production within AI systems, develops a typology tailored to materials contexts, and outlines principles for its detection and mitigation. By analyzing how premature search space pruning, overconfidence in negative predictions, and misaligned acquisition functions contribute to regret, this analysis reveals how current AI paradigms may systematically undervalue exploration in favor of short-term gains. The implications for materials AI practice are significant, calling for the design of regret-sensitive systems that better balance exploitation with the long-term costs of locked-in choices. Ultimately, embracing scientific regret as a core design constraint promises to foster more robust, reflective, and innovative approaches to autonomous materials research. Scientific regret is not merely an abstract philosophical concern but a practical barrier to genuine progress in materials science. When AI systems guide researchers away from promising chemistries or structures, the subsequent realization of a missed opportunity can stall entire research programs, waste limited experimental resources, and distort the collective knowledge base of the field. This failure mode is especially insidious because materials discovery operates in enormous design spaces where exhaustive enumeration is impossible and where negative predictions are rarely revisited once resources are committed elsewhere. By foregrounding scientific regret as a failure mode, this analysis seeks to reorient the community toward decision frameworks that explicitly account for the irreversible nature of many AI-influenced choices in materials selection.

In the field of artificial intelligence applied to materials science, a fundamental conflation persists in which exploration noise and scientific error are routinely conflated as interchangeable “mistakes” that must be minimized or eliminated to improve model performance. This paper proposes precise conceptual definitions that separate exploration noise—understood as stochastic variation deliberately or unavoidably introduced into decision-making processes to probe uncertain regions of materials design space—from scientific error, defined as any deviation from ground truth that reduces predictive fidelity, distorts mechanistic understanding, or precipitates incorrect materials decisions without any compensating epistemic gain. The distinction matters profoundly because the systematic elimination of exploration noise eradicates the very mechanism that drives discovery in high-dimensional, data-scarce materials landscapes. In contrast, misclassifying scientific error as mere noise allows systematic flaws to propagate undetected through autonomous discovery pipelines. To resolve this ambiguity, the present work offers a four-criterion framework grounded in intentionality, epistemic benefit, systematicity, and correctability that enables researchers to classify any observed deviation with conceptual clarity. Adoption of this framework carries immediate implications for materials AI practice: it demands new reporting standards that explicitly quantify and justify exploration noise, revised peer-review criteria that interrogate rather than penalize productive randomness, and a cultural shift that reframes stochasticity not as a defect to be denoised but as an essential epistemic resource for accelerating the discovery of novel materials with targeted functionalities.

Materials discovery remains painfully slow. Traditional human-driven experimentation, followed by offline machine-learning analysis, requires weeks or months per iteration and leaves vast regions of chemical space unexplored. This position paper argues that autonomous laboratories equipped with real-time ML feedback loops represent not an incremental improvement but a necessary paradigm shift for the future of materials engineering. In these systems, robotic platforms handle synthesis and characterization while ML models continuously update and steer the next experiment, closing the discovery loop in hours rather than weeks. The current paradigm relies on human-in-the-loop decision-making, batch experimentation, and post-hoc ML training. Autonomous laboratories reverse this: robots execute synthesis and characterization tasks, a real-time ML engine analyzes streaming data, and an acquisition function immediately proposes the next candidate, all without human intervention for routine decisions. Early demonstrations have already shown accelerated discovery of battery electrolytes, perovskites, and catalysts. Real-time ML feedback loops demand online learning, rigorous uncertainty quantification, rapid acquisition functions, multi-objective optimization, constraint handling, human oversight for safety, and seamless data streaming. We articulate seven foundational principles for closed-loop discovery: integration-first design, speed as a first-class constraint, uncertainty-driven exploration, graceful degradation, data provenance, modularity, and open standards. These principles address the technical, operational, and cultural barriers that still prevent widespread adoption. While challenges remain—high initial costs, instrument integration, and long-duration experiments—the community now possesses the necessary ML maturity, robotic hardware, and orchestration tools to overcome them. This position calls for coordinated investment in shared autonomous-lab infrastructure, open standards, and training programs so that closed-loop discovery becomes the default workflow across academia and industry. Only then can materials science deliver the energy, sustainability, and electronics breakthroughs society urgently needs.

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.