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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.

In the rapidly advancing domain of artificial intelligence applied to materials science, systems are frequently called upon to make critical decisions under conditions of substantial uncertainty, such as selecting which candidate material to synthesize next, which experiment to prioritize for evaluation, or which property to measure in a given campaign. A fundamental aspect that current materials AI approaches largely ignore is the phenomenon of regret—the realization, after the fact, that a different choice would have produced a superior outcome, often carrying emotional, cognitive, and practical costs for the decision-maker. Regret theory, originating in decision theory and economics, provides a powerful alternative lens for understanding choice under uncertainty by incorporating not only expected utilities but also the anticipation and experience of post-decision disappointment or rejoicing. This paper proposes a conceptual framework for regret-aware materials AI systems that explicitly integrates regret quantification, theoretical regret bounds, regret minimization objectives, regret-aware acquisition functions, and regret communication mechanisms into the decision-making pipeline. The framework further delineates four primary types of regret encountered in materials contexts—synthesis regret, measurement regret, discovery regret, and resource regret—each arising from the irreversible, sequential, and high-stakes nature of experimental materials research. By embedding these elements, the proposed framework shifts materials AI from a narrow focus on reward maximization toward systems that more closely mirror the nuanced realities of scientific decision-making, where the avoidance of avoidable regret becomes a central design goal. Ultimately, embracing regret awareness promises more robust exploration of vast material spaces, better alignment between AI recommendations and laboratory constraints, and enhanced trust between human researchers and autonomous systems, thereby accelerating genuine discovery while mitigating the hidden costs of overlooked alternatives.

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.