Inferring the dynamics of glass-forming liquids from static structure across thermal states
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915862634561536 |
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| author | Bessho, Hidemasa Kawasaki, Takeshi Shiba, Hayato |
| author_facet | Bessho, Hidemasa Kawasaki, Takeshi Shiba, Hayato |
| contents | In this study, we demonstrate the generalizability of graph neural networks in predicting the dynamic heterogeneity of model glass-forming liquids across different temperatures. While previous approaches have often been limited to making predictions at the specific temperatures used during training, we find that our proposed framework - T-BOTAN - enables interpolation to temperatures not included in the training set. We show that the dynamical behavior, the associated four-point correlations, and even the macroscopic temperature can be estimated with sufficient accuracy solely from static particle configurations at untrained temperatures. These results suggest that static configurations encode not only local structural features driving dynamic heterogeneity but also fundamental thermodynamic information. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13820 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Inferring the dynamics of glass-forming liquids from static structure across thermal states Bessho, Hidemasa Kawasaki, Takeshi Shiba, Hayato Soft Condensed Matter Disordered Systems and Neural Networks In this study, we demonstrate the generalizability of graph neural networks in predicting the dynamic heterogeneity of model glass-forming liquids across different temperatures. While previous approaches have often been limited to making predictions at the specific temperatures used during training, we find that our proposed framework - T-BOTAN - enables interpolation to temperatures not included in the training set. We show that the dynamical behavior, the associated four-point correlations, and even the macroscopic temperature can be estimated with sufficient accuracy solely from static particle configurations at untrained temperatures. These results suggest that static configurations encode not only local structural features driving dynamic heterogeneity but also fundamental thermodynamic information. |
| title | Inferring the dynamics of glass-forming liquids from static structure across thermal states |
| topic | Soft Condensed Matter Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2603.13820 |