Analysis of the control dataset demonstrated the method's ability to distinguish between different types of novelty. The separate treatment of chemical and structural axes enables nuanced assessment of materials novelty, allowing researchers to identify materials that are novel in either or both domains. The interpretability of the method was also demonstrated and allowed unusual density rankings to be understood. Application to the GNOME dataset revealed that their computational predictions tend toward chemical novelty, particularly through the incorporation of exotic elements, while showing mixed levels of structural novelty relative to a known corpus of lithium-containing compounds. This approach also highlighted several potential experimental targets with high chemical and structural novelty relative to the existing corpus of data.