Electric Polarization from Many-Body Neural Network Ansatz
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909229067010048 |
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| author | Li, Xiang Qian, Yubing Chen, Ji |
| author_facet | Li, Xiang Qian, Yubing Chen, Ji |
| contents | Ab initio calculation of dielectric response with high-accuracy electronic structure methods is a long-standing problem, for which mean-field approaches are widely used and electron correlations are mostly treated via approximated functionals. Here we employ a neural network wavefunction ansatz combined with quantum Monte Carlo to incorporate correlations into polarization calculations. On a variety of systems, including isolated atoms, one-dimensional chains, two-dimensional slabs, and three-dimensional cubes, the calculated results outperform conventional density functional theory and are consistent with the most accurate calculations and experimental data. Furthermore, we have studied the out-of-plane dielectric constant of bilayer graphene using our method and re-established its thickness dependence. Overall, this approach provides a powerful tool to consider electron correlation in the modern theory of polarization. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2307_02212 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Electric Polarization from Many-Body Neural Network Ansatz Li, Xiang Qian, Yubing Chen, Ji Chemical Physics Disordered Systems and Neural Networks Materials Science Computational Physics Ab initio calculation of dielectric response with high-accuracy electronic structure methods is a long-standing problem, for which mean-field approaches are widely used and electron correlations are mostly treated via approximated functionals. Here we employ a neural network wavefunction ansatz combined with quantum Monte Carlo to incorporate correlations into polarization calculations. On a variety of systems, including isolated atoms, one-dimensional chains, two-dimensional slabs, and three-dimensional cubes, the calculated results outperform conventional density functional theory and are consistent with the most accurate calculations and experimental data. Furthermore, we have studied the out-of-plane dielectric constant of bilayer graphene using our method and re-established its thickness dependence. Overall, this approach provides a powerful tool to consider electron correlation in the modern theory of polarization. |
| title | Electric Polarization from Many-Body Neural Network Ansatz |
| topic | Chemical Physics Disordered Systems and Neural Networks Materials Science Computational Physics |
| url | https://arxiv.org/abs/2307.02212 |