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In the rapidly advancing field of artificial intelligence for materials science, systems are routinely engineered to eliminate every form of resistance—tedious calculations, anomalous data points, conceptual dead-ends, and even the slow, uncomfortable process of reconciling contradictory physical intuitions. Yet not all resistance is wasteful. Scientific friction—the productive resistance, conceptual struggle, and material recalcitrance that forces deeper engagement—functions as an overlooked epistemic resource that sharpens understanding, exposes hidden assumptions, and drives genuine discovery. This failure mode analysis articulates scientific friction as the generative difficulty that AI increasingly erases through automation, smoothing, abstraction, and black-boxing. It distinguishes four epistemically valuable types of friction (conceptual, methodological, material, and social) that have historically propelled progress in materials design, inverse problems, and autonomous discovery pipelines. The paper then identifies four mechanisms by which contemporary AI systems remove these frictions and presents a typology of four resulting failure modes—conceptual smoothing, anomaly suppression, methodological opacity, and premature closure—that undermine the very understanding AI claims to accelerate. Drawing exclusively on the peer-reviewed literature in artificial intelligence for materials science, science and technology studies, and philosophy of science, the analysis proposes detection principles and mitigation strategies that allow researchers to preserve productive friction without sacrificing efficiency. By reframing friction not as a bug to be fixed but as a feature to be curated, this work offers a framework for designing friction-aware AI tools that sustain the epistemic depth required for transformative materials innovation rather than delivering only superficial, frictionless outputs.