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In the evolving landscape of materials artificial intelligence (AI), synthetic data emerges not merely as a technical augmentation but as a profound scientific intervention that reshapes the interpretive dynamics of knowledge generation. This manuscript develops a conceptual framework that interprets synthetic data as an intermediary layer facilitating interactions between empirical realities and algorithmic abstractions in materials science. This study synthesizes recent literature and examines how synthetic data influences epistemic trade-offs, such as those between data fidelity and model generalizability. It steers feedback structures within AI-driven discovery processes. The framework underscores systems-level insights into integrating generative models with domain-specific ontologies, highlighting ethical considerations in the curation of virtual datasets that mirror physical constraints without empirical grounding. Analytically, it explores the implications for accelerating materials innovation through enhanced representational capacities, while addressing potential distortions in scientific reasoning arising from over-reliance on simulated inputs. This interpretive approach reveals the transformative potential of synthetic data in reconfiguring the boundaries of human-AI collaboration, fostering a more reflexive understanding of material phenomena. Ultimately, the framework invites a reevaluation of data’s role in scientific inquiry, emphasizing integrative logics that balance innovation with epistemological integrity in the pursuit of advanced materials.

The application of artificial intelligence (AI) in materials science has revolutionized predictive modeling, enabling the efficient exploration of chemical spaces and the optimization of properties. However, the field’s dependence on finite experimental datasets has led to extensive synthetic data generation and dataset recycling, posing risks to model sustainability. This conceptual manuscript defines model collapse in materials AI as the gradual erosion of model performance and diversity stemming from repeated training on recycled or synthetically derived data. Informed by generative AI theories, it explores how overuse of datasets contributes to knowledge decay, in which models progressively favor averaged representations, diminishing their capacity to model atypical material behaviors or emergent properties. A new conceptual framework is presented that outlines the iterative processes of data recycling, synthetic augmentation, and decay escalation. This framework identifies reinforcing cycles that intensify representational biases and constrain exploratory potential in materials innovation. By integrating recent literature on AI instabilities and materials data challenges, the paper advocates a theoretical rethinking of data management to ensure the enduring utility of AI in materials science. Recent 2025 studies propose strategies such as golden ratio weighting and reinforcement-based synthetic data curation to avert collapse, underscoring the manuscript’s emphasis on proactive data governance for enduring AI utility in materials science. The discussion extends to broader AI ecosystems, stressing equilibrium between data utilization and knowledge preservation.