Search

AI-guided materials search increasingly relies on probabilistic and adaptive algorithms to navigate complex design spaces. Within these processes, early convergence emerges as a recurring dynamic wherein search trajectories stabilize around promising regions before exhaustive mapping of uncertainty landscapes occurs. This conceptual manuscript examines the interpretive costs associated with such premature stabilization, framing them not as isolated computational inefficiencies but as interconnected epistemic, structural, and innovation-limiting phenomena. Drawing on recent developments in Bayesian optimization, active learning, and equivariant graph representations for materials systems, the analysis examines how early convergence interacts with exploration-exploitation trade-offs, cascading into effects on knowledge breadth and discovery potential. A novel conceptual framework is advanced that conceptualizes these dynamics through feedback loops and trade-off structures, emphasizing systems-level insights into how algorithmic steering logics shape long-term trajectories in materials innovation. By focusing exclusively on interpretive and integrative dimensions, the contribution highlights the need for refined conceptual models that account for hidden costs embedded in convergence behaviors. This perspective encourages deeper reflection on the epistemic foundations of AI-assisted discovery without invoking empirical validation or predictive assertions.