Vision Transformers for X-ray Diffraction Patterns Analysis
Titouan Simonnet, Mame Diarra Fall, Sylvain Grangeon, Bruno Galerne
Abstract
Understanding materials properties depends largely on the ability to determine its components, and in particular its mineral phases. Powder X-ray diffraction (XRD) is a powerful tool for such purposes. This paper presents a Transformer-based vision model (ViT) for mineral phase identification, and proportion inference to quantify the mineral phases present in a material. Our analysis shows that the tokenization strategy is a critical step for XRD pattern analysis. The results obtained for both tasks are excellent and more robust than those obtained with a CNN. The proposed approach also makes it possible to introduce visualization tools for signal analysis, to better understand how information flows through the model and how data is classified or quantified.
BibTeX
@inproceedings{icassp2025_visiontransforme,
title = {Vision Transformers for X-ray Diffraction Patterns Analysis},
author = {Titouan Simonnet and Mame Diarra Fall and Sylvain Grangeon and Bruno Galerne},
booktitle = {ICASSP 2025},
year = {2025}
}