Message-Passing Neural Quantum States for the Homogeneous Electron Gas
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866912227073720320 |
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| author | Pescia, Gabriel Nys, Jannes Kim, Jane Lovato, Alessandro Carleo, Giuseppe |
| author_facet | Pescia, Gabriel Nys, Jannes Kim, Jane Lovato, Alessandro Carleo, Giuseppe |
| contents | We introduce a message-passing-neural-network-based wave function Ansatz to simulate extended, strongly interacting fermions in continuous space. Symmetry constraints, such as continuous translation symmetries, can be readily embedded in the model. We demonstrate its accuracy by simulating the ground state of the homogeneous electron gas in three spatial dimensions at different densities and system sizes. With orders of magnitude fewer parameters than state-of-the-art neural-network wave functions, we demonstrate better or comparable ground-state energies. Reducing the parameter complexity allows scaling to $N=128$ electrons, previously inaccessible to neural-network wave functions in continuous space, enabling future work on finite-size extrapolations to the thermodynamic limit. We also show the Ansatz's capability of quantitatively representing different phases of matter. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_07240 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Message-Passing Neural Quantum States for the Homogeneous Electron Gas Pescia, Gabriel Nys, Jannes Kim, Jane Lovato, Alessandro Carleo, Giuseppe Quantum Physics Strongly Correlated Electrons Nuclear Theory Computational Physics We introduce a message-passing-neural-network-based wave function Ansatz to simulate extended, strongly interacting fermions in continuous space. Symmetry constraints, such as continuous translation symmetries, can be readily embedded in the model. We demonstrate its accuracy by simulating the ground state of the homogeneous electron gas in three spatial dimensions at different densities and system sizes. With orders of magnitude fewer parameters than state-of-the-art neural-network wave functions, we demonstrate better or comparable ground-state energies. Reducing the parameter complexity allows scaling to $N=128$ electrons, previously inaccessible to neural-network wave functions in continuous space, enabling future work on finite-size extrapolations to the thermodynamic limit. We also show the Ansatz's capability of quantitatively representing different phases of matter. |
| title | Message-Passing Neural Quantum States for the Homogeneous Electron Gas |
| topic | Quantum Physics Strongly Correlated Electrons Nuclear Theory Computational Physics |
| url | https://arxiv.org/abs/2305.07240 |