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The integration of artificial intelligence (AI) and machine learning (ML) into materials science has revolutionized the discovery, design, and optimization of new materials, enabling accelerated predictions of properties and behaviors previously unattainable with traditional methods. However, the “black-box” nature of many advanced AI models poses significant challenges, including a lack of transparency that hinders scientific understanding, trust, and practical adoption in materials research. This narrative review explores the concept of interpretability in materials AI, focusing on what constitutes an “explanation” and how it should be conceptually evaluated. Drawing from recent advancements in explainable AI (XAI), we delineate definitions of explanations tailored to materials informatics, emphasizing their role in bridging computational predictions with physical insights. We examine thematic aspects such as intrinsic versus post-hoc interpretability methods, the multidimensional nature of explanations (e.g., local vs. global, feature-based vs. mechanistic), and conceptual frameworks for evaluation, including criteria like fidelity, comprehensibility, robustness, and domain-specific relevance. By synthesizing the literature, we highlight how explanations can enhance materials discovery across alloy design, catalyst development, and polymer engineering, while addressing gaps in current evaluation practices. The review underscores the need for standardized conceptual metrics that go beyond quantitative benchmarks to incorporate qualitative, human-centered assessments in materials science contexts. Ultimately, this work aims to guide researchers toward developing interpretable AI systems that not only predict but also elucidate underlying material phenomena, fostering a more insightful and ethical application of AI in materials innovation.

In the rapidly expanding domain of artificial intelligence applied to materials science, the relentless pursuit of optimal predictive performance has emerged as the central organizing principle. Yet, this very imperative creates a profound paradox: models that achieve near-perfect accuracy on benchmark tasks frequently erode the scientific understanding they purport to support. When optimization dominates, systems become hyper-specialized predictors that deliver engineering-grade outputs while concealing the mechanistic pathways essential to genuine discovery. Optimization in materials AI unquestionably achieves impressive feats such as accelerated property prediction, efficient virtual screening of vast chemical spaces, and practical utility in guiding experimental synthesis; however, these gains come at the expense of interpretability, robustness, generalizability, and the capacity to generate novel hypotheses about underlying physical laws. This critical critique isolates four interlocking problems inherent to over-optimization: prediction without explanation, in which flawless forecasts provide no causal or structural insight; fragile optimality, whereby peak performance on training distributions collapses under even modest shifts in material conditions; the exploration-exploitation trap, which locks research into incremental refinement of known chemistries at the cost of venturing into truly novel territories; and optimization as epistemic closure, where the declaration of state-of-the-art accuracy prematurely terminates further inquiry. The consequences for materials science are far-reaching, manifesting as stagnant theoretical progress despite benchmark improvements, brittle knowledge bases ill-suited to real-world deployment, systematic neglect of high-potential but uncertain discoveries, and the misallocation of computational and human resources toward marginal accuracy gains rather than foundational insight. Alternative frameworks that deliberately balance predictive power with explanatory depth—ranging from explicit Pareto optimization of accuracy against interpretability to explanation-forcing model designs and satisficing strategies—are therefore not optional enhancements but necessary correctives if artificial intelligence is to fulfill its promise as a genuine partner in scientific understanding rather than a mere engineering tool. By reframing the goals of materials AI away from singular optimality. Toward epistemic multiplicity, the field can escape the curse of optimality and reclaim the generative interplay between prediction and comprehension that has historically driven materials innovation.