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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.

In the evolving landscape of computational and data-driven materials engineering, the integration of machine learning and high-throughput methodologies has accelerated discovery processes, yet it introduces a paradox where rapid optimization often bypasses deep scientific understanding. This manuscript presents a systems theory perspective on black-box optimization in autonomous materials engineering, emphasizing closed-loop labs where AI-driven decisions guide experimentation without explicit interpretability. Drawing from materials informatics and representation learning, we identify the discovery acceleration paradox: enhanced efficiency in inverse design and property prediction erodes traditional epistemic structures, leading to reliance on opaque models. We introduce the "Epistemic Opaque Discovery System" (EODS) framework, which conceptualizes materials discovery as a layered network of data infrastructures, model architectures, and feedback mechanisms. This framework highlights trade-offs between optimization speed and interpretability, incorporating uncertainty quantification to mitigate risks in autonomous systems. Implications extend to simulation-experiment coupling and multimodal datasets, suggesting pathways for balanced computational workflows that preserve scientific insight amid black-box dominance. By reframing discovery pipelines, EODS offers a theoretical lens for engineering resilient AI ecosystems in materials science, fostering sustainable innovation without sacrificing foundational knowledge.