Machine learning-assisted close-set X-ray diffraction phase identification of transition metals

Maksim Zhdanov; Andrey Zhdanov

Machine learning-assisted close-set X-ray diffraction phase identification of transition metals

Materials Science 2023-05-26 v1 Artificial Intelligence Machine Learning

Authors: Maksim Zhdanov , Andrey Zhdanov

Abstract

Machine learning has been applied to the problem of X-ray diffraction phase prediction with promising results. In this paper, we describe a method for using machine learning to predict crystal structure phases from X-ray diffraction data of transition metals and their oxides. We evaluate the performance of our method and compare the variety of its settings. Our results demonstrate that the proposed machine learning framework achieves competitive performance. This demonstrates the potential for machine learning to significantly impact the field of X-ray diffraction and crystal structure determination. Open-source implementation: https://github.com/maxnygma/NeuralXRD.

Keywords

crystal structure prediction machine learning for materials science x-ray imaging and diffraction

Cite

@article{arxiv.2305.15410,
  title  = {Machine learning-assisted close-set X-ray diffraction phase identification of transition metals},
  author = {Maksim Zhdanov and Andrey Zhdanov},
  journal= {arXiv preprint arXiv:2305.15410},
  year   = {2023}
}

Comments

Published at the ICLR 2023 Workshop on Machine Learning for Materials, 5 pages, 1 figure

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