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In the rapidly evolving domain of artificial intelligence for materials science, path dependency remains a critically overlooked phenomenon that shapes both computational pipelines and the broader scientific enterprise. Algorithmic path dependency manifests when seemingly innocuous early choices in neural network initialization, training data ordering, hyperparameter selection, feature descriptor definition, or early stopping criteria create irreversible constraints on subsequent model behaviors and outputs, as evidenced in recurrent neural network architectures designed for heterogeneous materials. Scientific path dependency, by contrast, arises in the history and philosophy of science when initial decisions regarding research problems, material systems, theoretical frameworks, experimental protocols, or funding priorities lock research communities into particular trajectories, rendering alternative avenues increasingly difficult to pursue even when they might yield superior insights. This paper advances the theoretical claim that algorithmic path dependency propagates directly into scientific path dependency within materials AI, such that technical decisions made at the level of code and data become de facto determinants of which materials are discovered, which questions are asked, and which knowledge ultimately enters the scientific canon. The linkage operates through identifiable mechanisms, including output filtering, resource allocation, knowledge representation, and publication bias, each amplifying the long-term scientific consequences of early algorithmic commitments. By drawing upon foundational economic concepts of increasing returns and historical contingency alongside contemporary literature in machine learning for materials, this theoretical analysis proposes that materials AI researchers must explicitly recognize these dynamics to avoid unintended lock-in effects that could limit the diversity and robustness of future discoveries. The analysis further derives corollaries concerning constrained output diversity, the practical irreversibility of certain scientific paths, and the necessity of methodological pluralism, offering concrete implications for research practice, peer review standards, and community norms. Ultimately, this conceptual linkage reframes early algorithmic decisions not as mere technical details but as foundational scientific commitments whose consequences reverberate through the entire materials discovery ecosystem.