This work presents a parameter-free approach to materials novelty estimation that leverages mutual information to inform density calculations along both chemical and structural axes. By deriving influence functions directly from the mutual information profile of distance distributions, our method preserves the underlying signal from established distance metrics while adapting to the natural structure of the data. The approach successfully identifies meaningful novelty patterns in both a controlled dataset and characterizes novelty in a practical application to lithium-containing compounds from the GNOME dataset.