Previous work in materials novelty estimation has explored various methodological approaches, each with distinct limitations. Baird et al. previously used ElMD with a density-based approach to quantify chemical novelty in active learning campaigns . This approach, however, omitted structure and thus could not distinguish between polymorphs (same formula, different structure), which are an important axis of novelty. Additionally, their method computed material densities from multivariate Gaussian density functions over UMAP projections, which makes assumptions of the local structure of the data and introduces stochasticity. This stochasticity leads to inconsistent density calculations that vary with the chosen random seed. Other approaches using variational autoencoders have shown promise in learning structural patterns from X-ray diffraction data and identifying materials outside the training distribution . However, these methods require large training datasets that limit the method's applicability to small, specialized datasets. More recently, Gruver et al. used ElMD and LoStOP to define the chemical and structural novelty of materials generated with a large language model . While differentiation along chemical and structural axes was assessed, their approach relied on fixed, arbitrary cutoff values that may not reflect the natural distance distributions in a materials dataset.