Abstract
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.
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The first author is an undergrad student and the second author is an Assistant Professor and advisor of the first author. Though we have financial support from the institute to register the paper in the journal, we have not received explicit funding from any company. We have conflict of interest with IITR domain (iitr.ac.in) and TCS domain (tcs.com) as the second author has worked at these places.
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Chakraborty, A., Sharma, R. A deep crystal structure identification system for X-ray diffraction patterns. Vis Comput 38, 1275–1282 (2022). https://doi.org/10.1007/s00371-021-02165-8
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DOI: https://doi.org/10.1007/s00371-021-02165-8