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Materials informatics has achieved rapid progress in predicting composition–structure–property relationships, enabling accelerated screening, surrogate modeling, and exploration of high-dimensional design spaces. However, much of this success remains structurally grounded in correlational learning rather than in explanatory, transportable, or intervention-relevant forms of understanding. This conceptual manuscript argues that correlation-centric models, while often sufficient for ranking candidates under training-like conditions, are epistemically underpowered for high-stakes materials decisions such as processing optimization, microstructural control, deployment certification, and failure-sensitive design, where actions must remain defensible under distribution shift, partial observability, and changing constraints. In such settings, predictive accuracy alone does not establish decision legitimacy: a model may be correct for reasons that do not remain stable under deliberate intervention, confounding, or selection effects, thereby producing actionable recommendations without causal warrant. Motivated by recent developments in structural causal models, causal discovery, counterfactual inference, and invariant representation learning, this paper advances a theory-first reframing: materials AI should be treated as an epistemic instrument whose outputs must be qualified by the causal status they can legitimately support. We propose a novel framework—the Causal Warrant Ladder (CWL)—that classifies materials-model outputs into five ascending levels of causal legitimacy: associative regularities, transportable relations, mechanistic constraints, interventional guidance, and counterfactual design claims. CWL is paired with a Causal-Readiness Map, which specifies the minimal conceptual conditions required for upward movement on the ladder, including identifiability assumptions, invariance structure, intervention semantics, and decision stakes. By separating predictive competence from causal legitimacy, this roadmap provides a disciplined conceptual pathway beyond “black-box correlation” toward materials reasoning that supports robust and responsible design action.

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 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.