ContinuouSP: Generative Model for Crystal Structure Prediction with Invariance and Continuity
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917911255318528 |
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| author | Tone, Yuji Hanai, Masatoshi Kawamura, Mitsuaki Taura, Kenjiro Suzumura, Toyotaro |
| author_facet | Tone, Yuji Hanai, Masatoshi Kawamura, Mitsuaki Taura, Kenjiro Suzumura, Toyotaro |
| contents | The discovery of new materials using crystal structure prediction (CSP) based on generative machine learning models has become a significant research topic in recent years. In this paper, we study invariance and continuity in the generative machine learning for CSP. We propose a new model, called ContinuouSP, which effectively handles symmetry and periodicity in crystals. We clearly formulate the invariance and the continuity, and construct a model based on the energy-based model. Our preliminary evaluation demonstrates the effectiveness of this model with the CSP task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_02026 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ContinuouSP: Generative Model for Crystal Structure Prediction with Invariance and Continuity Tone, Yuji Hanai, Masatoshi Kawamura, Mitsuaki Taura, Kenjiro Suzumura, Toyotaro Machine Learning Materials Science The discovery of new materials using crystal structure prediction (CSP) based on generative machine learning models has become a significant research topic in recent years. In this paper, we study invariance and continuity in the generative machine learning for CSP. We propose a new model, called ContinuouSP, which effectively handles symmetry and periodicity in crystals. We clearly formulate the invariance and the continuity, and construct a model based on the energy-based model. Our preliminary evaluation demonstrates the effectiveness of this model with the CSP task. |
| title | ContinuouSP: Generative Model for Crystal Structure Prediction with Invariance and Continuity |
| topic | Machine Learning Materials Science |
| url | https://arxiv.org/abs/2502.02026 |