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The rapid proliferation of artificial intelligence (AI) techniques across materials science domains has delivered unprecedented predictive power and design acceleration. Yet, it has simultaneously engendered a previously under-examined failure mode: scientific lock-in through early AI adoption in materials domains. Scientific lock-in is defined here as the self-reinforcing entrenchment of specific AI methods, representations, or frameworks chosen early in the development of a subfield, rendering subsequent adoption of demonstrably superior alternatives prohibitively difficult even when their advantages become evident to the community. This failure mode arises through four interlocking mechanisms—increasing returns, switching costs, network effects, and institutionalization—and manifests across four distinct types: representational, methodological, data, and evaluation lock-in, each of which is shown to constrain the epistemic possibilities of materials research in characteristic ways. The resultant failure modes include suboptimal persistence of inferior approaches, innovation suppression of promising alternatives, comparative ignorance that prevents fair benchmarking, and collective regret in which the community recognizes the problem yet remains collectively unable to escape it. Detection principles grounded in observable indicators such as method concentration, citation bias, switching resistance, and comparative gaps are proposed, while mitigation principles centered on methodological pluralism, standardized comparisons, modular interoperability, community audits, and targeted funding for alternatives offer practical pathways to preserve long-term adaptability. By framing scientific lock-in as a distinct failure mode in materials AI, the present analysis urges the community to treat early adoption choices not merely as technical decisions but as high-stakes commitments whose downstream consequences must be deliberately managed if the field is to retain its capacity for genuine scientific progress.