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In the evolving landscape of computational and data-driven materials engineering, transfer learning has emerged as a pivotal strategy to address data scarcity and enhance predictive capabilities across diverse materials systems. This approach leverages pre-trained models from one materials class to inform modeling in another, capitalizing on shared representational structures within high-dimensional chemical and physical spaces. However, the conceptual boundaries of reusability remain underexplored, particularly in terms of how representational invariances and domain shifts influence cross-class applicability. This manuscript introduces a novel conceptual framework, termed the Reusability Boundary Architecture (RBA), which delineates the systemic interactions between data representations, model architectures, and discovery workflows in transfer learning paradigms. By integrating insights from materials informatics, graph neural networks, and uncertainty quantification, the RBA elucidates the epistemic trade-offs inherent in transferring knowledge across materials classes, such as from inorganic crystals to organic polymers or metallic alloys to ceramics. The framework emphasizes computational steering logics that dynamically adjust for feature misalignment and contextual divergences, fostering more robust integration of simulation and experimental pipelines. Implications for the field include enhanced design of multimodal datasets, refined autonomous discovery systems, and improved inverse materials engineering, ultimately accelerating innovation in sustainable materials development without relying on empirical validations. This work provides a theoretical foundation for navigating the reusability frontiers in computational materials science, promoting interdisciplinary synergies between machine learning and domain-specific knowledge.

Transfer learning has become a cornerstone of computational materials engineering, addressing the fundamental tension between the exponential growth of high-throughput simulation data and the persistent scarcity of high-fidelity experimental labels. By repurposing knowledge encoded in large-scale computational repositories—ranging from density-functional theory (DFT) databases to molecular dynamics trajectories—transfer learning enables accurate property prediction, inverse design, and autonomous discovery even in data-constrained regimes. This review synthesizes the field’s maturation from early domain-adaptation approaches in microstructure informatics to contemporary foundation-model strategies that span inorganic crystals, organic polymers, and hybrid interfaces. We trace the evolution of techniques including graph-neural-network (GNN) pre-training, multi-fidelity fusion, and structure-aware fine-tuning, while highlighting their deployment in closed-loop pipelines that couple simulation with robotic experimentation. Case studies drawn from battery electrolytes, high-entropy alloys, and 2D heterostructures illustrate how hierarchical transfer frameworks achieve chemical accuracy with orders-of-magnitude fewer labels than scratch-trained models. The synthesis reveals a unifying computational workflow: pre-train on universal descriptors, adapt via frozen or low-rank updates, and close the loop through uncertainty-guided active learning. This infrastructure-level perspective underscores transfer learning’s role in transforming materials engineering from a trial-and-error discipline into a predictive, self-optimizing ecosystem.