Identifying Zeolite Frameworks with a Machine Learning Approach

Abstract

Zeolites are microporous crystalline materials with highly regular framework structures consisting of molecular-sized pores and channels. The characteristic framework type of a zeolite is conventionally defined by combining information on its coordination sequences, vertex symbols, tiling, and transitivity information. Here we present a novel knowledge-based approach for zeolite framework type classification. We show the predicting abilities of a machine learning model that uses a nine-dimensional feature vector including novel topological descriptors obtained by computational geometry techniques, together with selected physical and chemical properties of zeolite crystals. Trained on the crystallographic structures of known zeolites, this model predicts the framework types of zeolite crystals with very high accuracy.