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A deep crystal structure identification system for X-ray diffraction patterns by Abhik Chakraborty; Raksha Sharma is a Computer Science article available to read on EtoBox.

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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 v

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Author

Abhik Chakraborty; Raksha Sharma

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)