Symmetry enforced solution of the many-body Schrödinger equation with deep neural network
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916271409332224 |
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| author | Li, Zhe Lu, Zixiang Li, Ruichen Wen, Xuelan Li, Xiang Wang, Liwei Chen, Ji Ren, Weiluo |
| author_facet | Li, Zhe Lu, Zixiang Li, Ruichen Wen, Xuelan Li, Xiang Wang, Liwei Chen, Ji Ren, Weiluo |
| contents | The integration of deep neural networks with the Variational Monte Carlo (VMC) method has marked a significant advancement in solving the Schrödinger equation. In this work, we enforce spin symmetry in the neural network-based VMC calculation with modified optimization target. Our method is designed to solve for the ground state and multiple excited states with target spin symmetry at a low computational cost. It predicts accurate energies while maintaining the correct symmetry in strongly correlated systems, even in cases where different spin states are nearly degenerate. Our approach also excels at spin-gap calculations, including the singlet-triplet gap in biradical systems, which is of high interest in photochemistry. Overall, this work establishes a robust framework for efficiently calculating various quantum states with specific spin symmetry in correlated systems, paving the way for novel discoveries in quantum science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01222 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Symmetry enforced solution of the many-body Schrödinger equation with deep neural network Li, Zhe Lu, Zixiang Li, Ruichen Wen, Xuelan Li, Xiang Wang, Liwei Chen, Ji Ren, Weiluo Chemical Physics Computational Physics The integration of deep neural networks with the Variational Monte Carlo (VMC) method has marked a significant advancement in solving the Schrödinger equation. In this work, we enforce spin symmetry in the neural network-based VMC calculation with modified optimization target. Our method is designed to solve for the ground state and multiple excited states with target spin symmetry at a low computational cost. It predicts accurate energies while maintaining the correct symmetry in strongly correlated systems, even in cases where different spin states are nearly degenerate. Our approach also excels at spin-gap calculations, including the singlet-triplet gap in biradical systems, which is of high interest in photochemistry. Overall, this work establishes a robust framework for efficiently calculating various quantum states with specific spin symmetry in correlated systems, paving the way for novel discoveries in quantum science. |
| title | Symmetry enforced solution of the many-body Schrödinger equation with deep neural network |
| topic | Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2406.01222 |