Search

The integration of artificial intelligence (AI) into materials science has accelerated the exploration of complex material behaviors and properties. Yet, the fragmented nature of materials knowledge often hinders seamless machine processing. This conceptual paper proposes a framework in which ontologies serve as dynamic intermediaries, facilitating the transformation of disparate material knowledge into forms that AI systems can actively engage with. By emphasizing interaction dynamics between ontological structures and AI processes, the framework highlights systems-level insights into how semantic representations enable adaptive knowledge flows, addressing epistemic challenges in data interoperability and contextual understanding. Drawing on recent literature, it synthesizes advancements in semantic web technologies and knowledge graphs, illustrating trade-offs in balancing formal rigor with computational flexibility. The proposal explores feedback structures that enable iterative refinement of knowledge representations, thereby fostering ethical considerations in AI-driven materials research. Through interpretive reasoning, it underscores how ontology-driven approaches can enhance the interpretability of AI outputs in materials contexts, such as property prediction and structure-property relationships. Ultimately, this framework envisions a more cohesive ecosystem in which materials knowledge becomes inherently machine-actionable, enabling integrative advancements without empirical validation. The discussion remains focused on conceptual steering logics, avoiding predictive assertions to maintain a purely theoretical lens.