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

2023/04/28 by Maksim Zhdanov, Zhdanov, Maksim, Andrey Zhdanov +1 · 1 citation
Engineering · Materials Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Physical sciences #Iron and Steelmaking Processes #Machine Learning (cs.LG) #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #X-ray Diffraction in Crystallography

paper · pdf · doi:10.48550/arxiv.2305.15410

openalex publication_date 2023/04/28 · openalex created_date 2023/05/27 · openalex updated_date 2026/07/28

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.

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