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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.00803 |
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| _version_ | 1866916641818804224 |
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| author | Wang, Hao Zhong, Jiajun Li, Yikun Zhang, Junrong Du, Rong |
| author_facet | Wang, Hao Zhong, Jiajun Li, Yikun Zhang, Junrong Du, Rong |
| contents | In this paper, a dataset of one-dimensional powder diffraction patterns was designed with new strategy to train Convolutional Neural Networks for predicting space groups. The diffraction pattern was calculated based on lattice parameters and Extinction Laws, instead of the traditional approach of generating it from a crystallographic database. This paper demonstrates that the new strategy is more effective than the conventional method. As a result, the model trained on the cubic and tetragonal training set from the newly designed dataset achieves prediction accuracy that matches the theoretical maximums calculated based on Extinction Laws. These results demonstrate that machine learning-based prediction can be both physically reasonable and reliable. Additionally, the model trained on our newly designed dataset shows excellent generalization capability, much better than the one trained on a traditionally designed dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00803 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Designing a Dataset for Convolutional Neural Networks to Predict Space Groups Consistent with Extinction Laws Wang, Hao Zhong, Jiajun Li, Yikun Zhang, Junrong Du, Rong Neural and Evolutionary Computing Data Analysis, Statistics and Probability In this paper, a dataset of one-dimensional powder diffraction patterns was designed with new strategy to train Convolutional Neural Networks for predicting space groups. The diffraction pattern was calculated based on lattice parameters and Extinction Laws, instead of the traditional approach of generating it from a crystallographic database. This paper demonstrates that the new strategy is more effective than the conventional method. As a result, the model trained on the cubic and tetragonal training set from the newly designed dataset achieves prediction accuracy that matches the theoretical maximums calculated based on Extinction Laws. These results demonstrate that machine learning-based prediction can be both physically reasonable and reliable. Additionally, the model trained on our newly designed dataset shows excellent generalization capability, much better than the one trained on a traditionally designed dataset. |
| title | Designing a Dataset for Convolutional Neural Networks to Predict Space Groups Consistent with Extinction Laws |
| topic | Neural and Evolutionary Computing Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2411.00803 |