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

arXiv: https://arxiv.org/abs/2305.15410 · HTML: https://mlsanity.com/paper/2305.15410/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 28 Apr 2023.

## Builds on (most cited first, 1 of 1)

- [Exploring Supervised Machine Learning for Multi-Phase Identification and Quantification from Powder X-Ray Diffraction Spectra](https://mlsanity.com/paper/2211.08591/) (2022)

## Abstract (arXiv)

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.