XRD-VisionTransformer: Effective Multiphase Identification Framework for X-ray Diffraction Patterns.

Area of Science:

• Materials Science
• Crystallography
• Machine Learning

Background:

• X-ray diffraction (XRD) is crucial for identifying crystalline phases in mixtures.
• Traditional XRD phase identification is slow and labor-intensive.
• Machine learning, particularly Convolutional Neural Networks (CNNs), shows promise but struggles with long-range dependencies in multiphase XRD data.

Purpose of the Study:

• To develop an advanced machine learning model for accelerated and accurate multiphase identification in XRD patterns.
• To overcome the limitations of CNNs in capturing long-range dependencies and Vision Transformers (ViTs) in handling XRD data characteristics.

Main Methods:

• Proposed XRD-VisionTransformer (XViT), a novel network integrating ViT architecture with XRD-specific adaptations.
• Introduced a statistical positional embedding module to encode crystallographic priors, improving performance on smaller datasets.
• Implemented a deep classifier tail to capture inter-peak dependencies for enhanced phase identification.

Main Results:

• XViT demonstrated superior performance in multiphase identification of XRD patterns compared to traditional CNN and ViT models.
• The statistical positional embedding module enhanced ViT's robustness to dataset size.
• The deep classifier tail effectively captured inter-peak relationships, leading to improved phase identification accuracy.

Conclusions:

• XViT offers a significant advancement in automated XRD phase identification, particularly for complex multiphase mixtures.
• The model's design effectively addresses the unique challenges of applying transformer architectures to XRD data.
• XViT provides a more efficient and accurate solution for crystalline phase analysis in materials science.