A deep crystal structure identification system for X-ray diffraction patterns

The experimental purpose of X-ray diffraction is to analyze crystalline material structure at the atomic and molecular levels. Such experiments are known as X-ray crystallography. Traditionally, human experts do it with some domain knowledge. X-ray crystallography is useful in numerous domains, e.g., physics, chemistry, and biology. It is tough to own manual physics of diffraction patterns to see a crystal structure with a colossal data set. A convolutional neural network (CNN) is a deep neural network that maps an input image into a high-dimensional space. CNN produces an affordable function for image classification. This paper uses an extension of the convolutional neural network to predict crystal structure from diffraction patterns. We propose a machine-enabled method to predict crystallographic size and space group from a limited number of XRD patterns for small films. We overcome the problem of scarce data within the development of building materials by combining the learning model of moderately monitored equipment, a physics information-enhancing strategy using data generated from the Inorganic Crystal Structure Database, and test data. We compare our approach with a large variety of typical addition as modern machine learning-based classification techniques for crystal structure prediction. Results show that our proposed system outperforms all the baselines by a significant margin for the crystal structure prediction task. Results also show the impact of the number of layers in the all-convolutional neural network architecture for crystal structure prediction.