Permutation invariant multi-scale full quantum neural network wavefunction
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
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| _version_ | 1866914388081901568 |
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| author | Cai, Pengzhen Qian, Yubing Deng, Li Fu, Weizhong Yang, Lei Sun, Zhiyu Li, Xin-Zheng Wang, En-Ge Chen, Liangwen Ren, Weiluo Chen, Ji |
| author_facet | Cai, Pengzhen Qian, Yubing Deng, Li Fu, Weizhong Yang, Lei Sun, Zhiyu Li, Xin-Zheng Wang, En-Ge Chen, Liangwen Ren, Weiluo Chen, Ji |
| contents | Solving the intricate quantum behavior of interacting particles is key to unlocking the mysteries of condensed matter, but capturing their complex correlations across different scales remains a monumental challenge. We introduce a neural network framework that overcomes this barrier by modeling the full quantum wavefunction of a system, including electrons, nuclei and muons, directly capturing the full quantum effects beyond the Born-Oppenheimer approximation. The neural network approximates joint wavefunction of different interacting particles with a rigorous handling of permutation invariance, enabling simultaneous treatment of nuclear quantum effects and electron-nucleus-muon couplings without explicit excited states. Validated on molecular systems, this approach offers a computationally feasible way to model full quantum phenomena in complex many-body systems, establishing a direct connection between fundamental particle properties and emergent material behavior. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12233 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Permutation invariant multi-scale full quantum neural network wavefunction Cai, Pengzhen Qian, Yubing Deng, Li Fu, Weizhong Yang, Lei Sun, Zhiyu Li, Xin-Zheng Wang, En-Ge Chen, Liangwen Ren, Weiluo Chen, Ji Chemical Physics Solving the intricate quantum behavior of interacting particles is key to unlocking the mysteries of condensed matter, but capturing their complex correlations across different scales remains a monumental challenge. We introduce a neural network framework that overcomes this barrier by modeling the full quantum wavefunction of a system, including electrons, nuclei and muons, directly capturing the full quantum effects beyond the Born-Oppenheimer approximation. The neural network approximates joint wavefunction of different interacting particles with a rigorous handling of permutation invariance, enabling simultaneous treatment of nuclear quantum effects and electron-nucleus-muon couplings without explicit excited states. Validated on molecular systems, this approach offers a computationally feasible way to model full quantum phenomena in complex many-body systems, establishing a direct connection between fundamental particle properties and emergent material behavior. |
| title | Permutation invariant multi-scale full quantum neural network wavefunction |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2603.12233 |