GCN-Based Framework for Materials Screening and Phase Identification

Zhenkai Qin

doi:10.3390/ma18050959

GCN-Based Framework for Materials Screening and Phase Identification

2025-02-21

0

OA

AI

DOI：10.3390/ma18050959

Original

Save

View PDF

Abstract

En 中文

This study proposes a novel framework using graph convolutional networks to analyze and interpret X-ray diffraction patterns, addressing challenges in phase identification for multi-phase materials. By representing X-ray diffraction patterns as graphs, the framework captures both local and global relationships between diffraction peaks, enabling accurate phase identification even in the presence of overlapping peaks and noisy data. The framework outperforms traditional machine learning models, achieving a precision of 0.990 and a recall of 0.872. This performance is attained with minimal hyperparameter tuning, making it scalable for large-scale material discovery applications. Data augmentation, including synthetic data generation and noise injection, enhances the model's robustness by simulating real-world experimental variations. However, the model's reliance on synthetic data and the computational cost of graph construction and inference remain limitations. Future work will focus on integrating real experimental data, optimizing computational efficiency, and exploring lightweight architectures to improve scalability for high-throughput applications.

Keywords:

X-ray diffraction pattern analysis

graph-based phase identification

deep learning for crystallography

diffraction peak correlation

GCN-Based Framework for Materials Screening and Phase Identification

Abstract

AI Summary

Journal

Organization