Natural-Orbital-Based Neural Network Configuration Interaction
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Thirion, Louis Schmerwitz, Yorick L. A. Kroesbergen, Max Levi, Gianluca Jónsson, Elvar Ö. Bilous, Pavlo Jónsson, Hannes Hansmann, Philipp |
| author_facet | Thirion, Louis Schmerwitz, Yorick L. A. Kroesbergen, Max Levi, Gianluca Jónsson, Elvar Ö. Bilous, Pavlo Jónsson, Hannes Hansmann, Philipp |
| contents | Selective configuration interaction methods approximate correlated molecular ground- and excited states by considering only the most relevant Slater determinants in the expansion. While a recently proposed neural-network-assisted approach efficiently identifies such determinants, the procedure typically relies on canonical Hartree-Fock orbitals, which are optimized only at the mean-field level. Here we assess approximate natural orbitals - eigenfunctions of the one-particle density matrix computed from intermediate many-body eigenstates - as an alternative. Across our benchmarks for H$_2$O, NH$_3$, CO, and C$_3$H$_8$ we see a consistent reduction in the required determinants for a given accuracy of the computed correlation energy compared to full configuration interaction calculations. Our results confirm that even approximate natural orbitals constitute a simple yet powerful strategy to enhance the efficiency of neural-network-assisted configuration interaction calculations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_27665 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Natural-Orbital-Based Neural Network Configuration Interaction Thirion, Louis Schmerwitz, Yorick L. A. Kroesbergen, Max Levi, Gianluca Jónsson, Elvar Ö. Bilous, Pavlo Jónsson, Hannes Hansmann, Philipp Chemical Physics Selective configuration interaction methods approximate correlated molecular ground- and excited states by considering only the most relevant Slater determinants in the expansion. While a recently proposed neural-network-assisted approach efficiently identifies such determinants, the procedure typically relies on canonical Hartree-Fock orbitals, which are optimized only at the mean-field level. Here we assess approximate natural orbitals - eigenfunctions of the one-particle density matrix computed from intermediate many-body eigenstates - as an alternative. Across our benchmarks for H$_2$O, NH$_3$, CO, and C$_3$H$_8$ we see a consistent reduction in the required determinants for a given accuracy of the computed correlation energy compared to full configuration interaction calculations. Our results confirm that even approximate natural orbitals constitute a simple yet powerful strategy to enhance the efficiency of neural-network-assisted configuration interaction calculations. |
| title | Natural-Orbital-Based Neural Network Configuration Interaction |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2510.27665 |