Deep Learning Classification of Crystal Structures Utilizing Wyckoff Positions

Abstract

In materials science, crystal lattice structures are the primary metrics used to measure the structure–property paradigm of a crystal structure. Crystal compounds are understood by the number of various atomic chemical settings, which are associated with Wyckoff sites. In crystallography, a Wyckoff site is a point of conjugate symmetry. Therefore, features associated with the various atomic settings in a crystal can be fed into the input layers of deep learning models. Methods to analyze crystals using Wyckoff sites can help to predict crystal structures. Hence, the main contribution of our article is the classification of crystal classes using Wyckoff sites. The presented model classifies crystals using diffraction images and a deep learning method. The model extracts feature groups including crystal Wyckoff features and crystal geometry. In this article, we present a deep learning model to predict the stage of the crystal structure–property. The lattice parameters and the structure–property commotion values are used as inputs into the deep learning model for training. The structure–property value of a crystal with a lattice width value of one-half millimeter on average is used for learning. The model attains a considerable increase in speed and precision for the real structure–property prediction. The experimental results prove that our proposed model has a fast learning curve, and can have a key role in predicting the structure–property of compound structures.