Artificial neural network methods for the prediction of framework crystal structures of zeolites from XRD data

Abstract

Extracting information about the structures of zeolites and other crystalline materials from X-ray diffraction (XRD) data simply by using statistical methods may provide an impetus for the discovery and identification of unknown materials. In this study, the possibility of using artificial neural network methods for relating framework crystal structures to XRD data reported in literature was investigated. Generalized Regression Neural Networks and Radial Basis Function-Based Neural Networks were utilized in the investigations. The results obtained by neural networks, using fivefold cross validation technique, were compared to the actual values as well as to those determined by multilinear regression. The predictions made by these neural network methods were, in general, more reliable than those performed by regression. The best predictions were achieved for the estimation of the framework densities of zeolites, which provided quite small deviations from the actual values.