Electron-Informed Coarse-Graining Molecular Representation Learning for Real-World Molecular Physics
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914311764443136 |
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| author | Na, Gyoung S. Park, Chanyoung |
| author_facet | Na, Gyoung S. Park, Chanyoung |
| contents | Various representation learning methods for molecular structures have been devised to accelerate data-driven chemistry. However, the representation capabilities of existing methods are essentially limited to atom-level information, which is not sufficient to describe real-world molecular physics. Although electron-level information can provide fundamental knowledge about chemical compounds beyond the atom-level information, obtaining the electron-level information in real-world molecules is computationally impractical and sometimes infeasible. We propose a method for learning electron-informed molecular representations without additional computation costs by transferring readily accessible electron-level information about small molecules to large molecules of our interest. The proposed method achieved state-of-the-art prediction accuracy on extensive benchmark datasets containing experimentally observed molecular physics. The source code for HEDMoL is available at https://github.com/ngs00/HEDMoL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_07087 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Electron-Informed Coarse-Graining Molecular Representation Learning for Real-World Molecular Physics Na, Gyoung S. Park, Chanyoung Chemical Physics Artificial Intelligence Machine Learning Computational Physics Various representation learning methods for molecular structures have been devised to accelerate data-driven chemistry. However, the representation capabilities of existing methods are essentially limited to atom-level information, which is not sufficient to describe real-world molecular physics. Although electron-level information can provide fundamental knowledge about chemical compounds beyond the atom-level information, obtaining the electron-level information in real-world molecules is computationally impractical and sometimes infeasible. We propose a method for learning electron-informed molecular representations without additional computation costs by transferring readily accessible electron-level information about small molecules to large molecules of our interest. The proposed method achieved state-of-the-art prediction accuracy on extensive benchmark datasets containing experimentally observed molecular physics. The source code for HEDMoL is available at https://github.com/ngs00/HEDMoL. |
| title | Electron-Informed Coarse-Graining Molecular Representation Learning for Real-World Molecular Physics |
| topic | Chemical Physics Artificial Intelligence Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2602.07087 |