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The growing integration of artificial intelligence (AI) into materials science has substantially accelerated materials discovery and property prediction. Yet, the explanations produced by these systems often exhibit systematic failures that undermine their epistemic reliability. Despite increased attention to explainable AI, existing studies address explanation shortcomings in a fragmented, tool-centric manner, leaving unresolved questions about their scientific legitimacy. This conceptual manuscript introduces a unified theoretical framework for understanding failure modes in materials AI explanations as emergent properties of interaction dynamics between algorithmic representations, data ontologies, and domain epistemologies. Synthesizing literature, we identify three recurrent clusters of explanation failure—representational distortions, inferential misalignments, and contextual dissonances—each arising from structural trade-offs in model design, training, and deployment. To address these challenges, we articulate prevention principles as steering logics that operate through feedback structures, enabling recalibration of explanations without constraining predictive performance. Analytical implications demonstrate how explanation failures influence interpretive confidence, knowledge production, and ethical decision-making across materials research workflows. By reframing explanation failure as a diagnostic signal rather than a technical defect, the framework advances a systems-level understanding of AI explanations. It provides conceptual guidance for cultivating more trustworthy and epistemically aligned AI practices in materials science.