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

TL;DR

This paper presents a machine learning framework for predicting crystal structure phases of transition metals and their oxides from X-ray diffraction data, showing competitive performance and potential for advancing crystal structure determination.

Contribution

The paper introduces a novel machine learning approach specifically designed for phase identification in transition metals using X-ray diffraction data.

Findings

Achieves competitive accuracy in phase prediction

Demonstrates the effectiveness of machine learning in X-ray diffraction analysis

Provides an open-source implementation for reproducibility

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.