XRDMamba: Large-scale Crystal Material Space Group Identification with Selective State Space Model
Authors: 
Liheng Yu, Pengkun Wang, Zhe Zhao, Zhongchao Yi, Sun Nan, Di Wu, Yang WangAuthors Info & Claims
Liheng Yu, Pengkun Wang, Zhe Zhao, Zhongchao Yi, Sun Nan, Di Wu, Yang WangAuthors Info & ClaimsPages 4233 - 4237
Abstract
In material science, the properties of crystalline materials largely depend on their structures, and space group is a key descriptor of crystal structure. With the rapid advancement of deep learning, the traditional artificial structure analysis method based on X-ray diffraction (XRD) has become cumbersome and is being gradually supplanted by neural networks. However, existing models are too simplistic and lack a comprehensive understanding of material structure. Our approach XRDMamba integrates chemical knowledge and presents a fresh crystal planes perspective on XRD data. We also introduce a knowledge-driven model for space group identification tasks. We have thoroughly analyzed our approach through numerous experiments, observing its SOTA performance and excellent generalization capabilities. The code is available in ~https://github.com/baigeiguai/XRDMamba.
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Index Terms
- XRDMamba: Large-scale Crystal Material Space Group Identification with Selective State Space Model
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Published: 21 October 2024
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