Classification of apatite structures via topological data analysis: a framework for a 'Materials Barcode' representation of structure maps.

ABSTRACT: This paper introduces the use of topological data analysis (TDA) as an unsupervised machine learning tool to uncover classification criteria in complex inorganic crystal chemistries. Using the apatite chemistry as a template, we track through the use of persistent homology the topological connectivity of input crystal chemistry descriptors on defining similarity between different stoichiometries of apatites. It is shown that TDA automatically identifies a hierarchical classification scheme within apatites based on the commonality of the number of discrete coordination polyhedra that constitute the structural building units common among the compounds. This information is presented in the form of a visualization scheme of a barcode of homology classifications, where the persistence of simila

SUBMITTER: Broderick S 

PROVIDER: S-EPMC8172868 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature