Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.23897 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917361012965376 |
|---|---|
| author | Wu, Teng Zhou, Qi Zheng, Huangjie Xie, Hehu Xu, Zhenli |
| author_facet | Wu, Teng Zhou, Qi Zheng, Huangjie Xie, Hehu Xu, Zhenli |
| contents | We present an improved version of the sum-of-Gaussians tensor neural network (SOG-TNN) architecture for solving many-electron Schrödinger equation for one-dimensional soft-Coulomb systems. Model reduction techniques are introduced to reduce the number of tensor-factorized bases under the SOG approximation of the kernel. The Slater determinant ansatz is employed so that the anti-symmetric property of the wave function can be strictly preserved. Numerical results show that the SOG-TNN achieves high accuracy with remarkably small basis sizes. Robust spectral convergence with respect to the basis size is also observed, consistently characterized by a mixed algebraic-exponential model for the error decay. These findings validate that the SOG-TNN architecture provides an ultra-efficient and low-rank representation of complex multi-electron wave functions, shedding light on high-fidelity quantum calculations in larger-scale many-electron systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_23897 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Spectral convergence of sum-of-Gaussians tensor neural networks for many-electron Schrödinger equation Wu, Teng Zhou, Qi Zheng, Huangjie Xie, Hehu Xu, Zhenli Chemical Physics We present an improved version of the sum-of-Gaussians tensor neural network (SOG-TNN) architecture for solving many-electron Schrödinger equation for one-dimensional soft-Coulomb systems. Model reduction techniques are introduced to reduce the number of tensor-factorized bases under the SOG approximation of the kernel. The Slater determinant ansatz is employed so that the anti-symmetric property of the wave function can be strictly preserved. Numerical results show that the SOG-TNN achieves high accuracy with remarkably small basis sizes. Robust spectral convergence with respect to the basis size is also observed, consistently characterized by a mixed algebraic-exponential model for the error decay. These findings validate that the SOG-TNN architecture provides an ultra-efficient and low-rank representation of complex multi-electron wave functions, shedding light on high-fidelity quantum calculations in larger-scale many-electron systems. |
| title | Spectral convergence of sum-of-Gaussians tensor neural networks for many-electron Schrödinger equation |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2603.23897 |