The materials science field has witnessed an expansion of computational and experimental data, with significant resources devoted to developing and maintaining comprehensive data repositories . These databases, which now contain several million materials, have enabled rapid computational screening for high performing materials using machine learning . While claims of "new" materials frequently appear in the literature, the field lacks robust methods to quantify and assess the novelty of these new materials relative to what is known. It is likely that much of the low hanging fruit in the materials space has been picked and that future high-performing materials will need to be sought after in less explored regions. This necessitates the development of methods to assess and quantify relative novelty in materials databases.