Machine learning-assisted close-set X-ray diffraction phase identification of transition metals. (arXiv:2305.15410v1 [cond-mat.mtrl-sci])

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.