Machine learning approach for structure-based zeolite classification

Abstract

Application of knowledge discovery methods to crystal structure databases is an emerging research area of materials science that is playing an important role in facilitating data analysis. This study is aimed at combining computational geometry methods with machine learning algorithms for classification of inorganic solid materials of known structure. Zeolite crystals are used for the pilot study where a model based on the topology is developed for classification of the compound by mineral name and by zeolite framework type. The topological descriptors are derived from the Delaunay tessellation for 220 zeolites contained in the inorganic crystal structure database. This zeolite-structure-predictor (ZSP) is trained for classifying this set of selected zeolite crystals into 22 different types of minerals and into 13 framework types. The ZSP is based on the random forest algorithm and contains attributes of Delaunay simplex properties such as tetrahedrality index, frequency of simplex occurrence, and site occupation probability. The ZSP is able to obtain classification in this multitude of classes with more than 81% of correctly classified instances based on framework type. The model shows that the classification into framework types is superior, and that the classification into mineral names is not structurally unique.