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In the rapidly advancing field of artificial intelligence for materials science, a persistent and underappreciated limitation has emerged: the overwhelming emphasis on identifying and deploying a single “best” model that maximizes predictive accuracy for properties such as band gaps, formation energies, or mechanical strengths, while largely neglecting the epistemic value of algorithmic diversity across model collections. This paper articulates the theoretical claim that algorithmic diversity functions as a core scientific robustness mechanism, independent of any marginal gains in accuracy, by enabling collective coverage of hypothesis space, resilience to distribution shifts, and more reliable knowledge generation in the face of inherent uncertainties in materials data and modeling assumptions. To operationalize this insight, the work proposes a novel conceptual framework consisting of five interlocking components—diversity dimensions, metrics, generation strategies, robustness linkages, and evaluation protocols—that together redefine how diverse model collections should be designed, assessed, and deployed in materials discovery pipelines. The framework further delineates five distinct types of diversity (architectural, representational, initialization, data-centric, and objective) that each contribute unique robustness benefits when applied to materials-specific challenges such as inverse design or multiscale modeling. By shifting the community’s focus from solitary model optimization to the deliberate cultivation of diverse algorithmic ecosystems, the implications extend to revised authorship practices, peer-review standards, and the establishment of diversity-aware benchmarks, ultimately positioning algorithmic diversity as an essential epistemic virtue for trustworthy, generalizable materials AI.