Mutual information informed novelty estimation of materials along chemical and structural axes
Year: 2025
Type: article
Abstract: 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.
Source: Digital Discovery
DOI: 10.1039/d5dd00167f
Authors: Andrew R. Falkowski, Taylor D. Sparks
Institution: University of Utah
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411165022