In this work, we present a simple, parameter-free method of assessing materials novelty along chemical and structural axes based on a mutual information (MI) informed density function. MI quantifies how much knowing the position of one material tells us about the positions of other materials, with higher values indicating stronger relationships between nearby points. By examining how MI changes with distance, one can establish objective criteria for determining meaningful neighborhoods and influence between materials without requiring predetermined parameters or assumptions about the underlying distribution. This approach preserves signal from the underlying distance metrics while adapting to the natural structure of the data. Through the use of density estimation, our method intuitively weighs closer neighbors more heavily than distant ones, reflecting how researchers typically assess data density. This method provides researchers with a tool for evaluating candidate materials along both chemical and structural axes, enabling more informed decisions in materials discovery campaigns. Furthermore, it offers a foundation for incorporating novelty considerations into active learning strategies.