SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the Crystal Symmetry Classification
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917923921068032 |
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| author | Cao, Bin Liu, Yang Zheng, Zinan Tan, Ruifeng Li, Jia Zhang, Tong-yi |
| author_facet | Cao, Bin Liu, Yang Zheng, Zinan Tan, Ruifeng Li, Jia Zhang, Tong-yi |
| contents | Spectroscopic data, particularly diffraction data, contain detailed crystal and microstructure information and thus are crucial for materials discovery. Powder X-ray diffraction (XRD) patterns are greatly effective in identifying crystals. Although machine learning (ML) has significantly advanced the analysis of powder XRD patterns, the progress is hindered by a lack of training data. To address this, we introduce SimXRD, the largest open-source simulated XRD pattern dataset so far, to accelerate the development of crystallographic informatics. SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations. We find that the crystal symmetry inherently follows a long-tailed distribution and evaluate 21 sequence learning models on SimXRD. The results indicate that existing neural networks struggle with low-frequency crystal classifications. The present work highlights the academic significance and the engineering novelty of simulated XRD patterns in this interdisciplinary field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_15469 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the Crystal Symmetry Classification Cao, Bin Liu, Yang Zheng, Zinan Tan, Ruifeng Li, Jia Zhang, Tong-yi Materials Science Spectroscopic data, particularly diffraction data, contain detailed crystal and microstructure information and thus are crucial for materials discovery. Powder X-ray diffraction (XRD) patterns are greatly effective in identifying crystals. Although machine learning (ML) has significantly advanced the analysis of powder XRD patterns, the progress is hindered by a lack of training data. To address this, we introduce SimXRD, the largest open-source simulated XRD pattern dataset so far, to accelerate the development of crystallographic informatics. SimXRD comprises 4,065,346 simulated powder X-ray diffraction patterns, representing 119,569 distinct crystal structures under 33 simulated conditions that mimic real-world variations. We find that the crystal symmetry inherently follows a long-tailed distribution and evaluate 21 sequence learning models on SimXRD. The results indicate that existing neural networks struggle with low-frequency crystal classifications. The present work highlights the academic significance and the engineering novelty of simulated XRD patterns in this interdisciplinary field. |
| title | SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the Crystal Symmetry Classification |
| topic | Materials Science |
| url | https://arxiv.org/abs/2406.15469 |