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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.

In the evolving landscape of computational and data-driven materials engineering, machine learning techniques have revolutionized the discovery and optimization of materials by leveraging vast datasets to identify patterns and correlations. However, this reliance on correlation-driven approaches often overlooks the underlying causal mechanisms that govern material properties and behaviors, leading to inherent limitations in the generalizability and robustness of designed materials. This manuscript explores the conceptual boundaries of optimization strategies that prioritize statistical associations over causal understanding within materials informatics ecosystems. We introduce a novel conceptual framework, termed the Correlation Boundary Architecture (CBA), which delineates the epistemic constraints imposed by correlation-centric pipelines in materials design. The CBA integrates representation learning, inference dynamics, and feedback structures to highlight how data-driven optimizations can falter in extrapolative scenarios, such as novel chemical spaces or extreme conditions. By synthesizing recent advancements in graph neural networks, high-throughput computations, and uncertainty quantification, we articulate the trade-offs between computational efficiency and causal fidelity. Implications extend to autonomous discovery systems and inverse design paradigms, suggesting pathways for hybrid frameworks that mitigate correlation biases through enhanced interpretive layers. This work underscores the need for computational steering logics that balance correlative power with causal awareness, fostering more resilient materials engineering practices.