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The applicability domain in materials artificial intelligence (AI) represents a fundamental epistemic boundary, beyond which predictive claims lose their scientific legitimacy. Rather than viewing it as a mere technical metric of model performance, this perspective frames the applicability domain as a decision boundary that demarcates regions where AI outputs are conditionally meaningful from those that demand principled silence. In materials science, where heterogeneous chemistries, structures, and processing protocols create complex epistemic landscapes, AI accelerates discovery, but risks overreach through unjustified extrapolation. We introduce a novel theory of scientific silence in materials AI, emphasizing restraint as an active epistemic virtue. This boundary-based framework maps claim types to required warrants, identifying interior regions for warranted predictions, boundary zones for conditional application, and exterior spaces where silence prevents hazard. By posing questions about the limits of generalization and the costs of misplaced confidence, the framework highlights how ignoring boundaries can turn AI from an accelerator into a source of epistemic risk. Ultimately, embracing silence fosters more robust materials innovation, ensuring AI serves as a tool for knowledge rather than illusion.