This work presents a parameter-free method for estimating materials novelty along chemical and structural axes using mutual information informed density functions. The approach quantifies novelty by analyzing how MI changes with distance between materials, establishing objective criteria for determining meaningful neighborhoods without requiring predetermined parameters. We demonstrate the method's effectiveness using two case studies: a control dataset of materials with varying degrees of similarity and a practical application analyzing lithium-containing compounds from the GNOME dataset relative to known materials. The method successfully identifies meaningful patterns of novelty in both chemical and structural domains while providing interpretable results that align with materials science intuition. This framework offers researchers a quantitative tool for assessing candidate materials against existing knowledge bases and could support more informed selection of synthesis targets in materials discovery campaigns.