Synergy between deep neural networks and the variational Monte Carlo method for small $^4He_N$ clusters
We introduce a neural network-based approach for modeling wave functions that satisfy Bose-Einstein statistics. Applying this model to small $^4He_N$ clusters (with N ranging from 2 to 14 atoms), we accurately predict ground state energies, pair density functions, and two-body contact parameters $C^...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
2023-12-01
|
| Series: | Quantum |
| Online Access: | https://quantum-journal.org/papers/q-2023-12-18-1209/pdf/ |
| _version_ | 1797337880547295232 |
|---|---|
| author | William Freitas S. A. Vitiello |
| author_facet | William Freitas S. A. Vitiello |
| author_sort | William Freitas |
| collection | DOAJ |
| description | We introduce a neural network-based approach for modeling wave functions that satisfy Bose-Einstein statistics. Applying this model to small $^4He_N$ clusters (with N ranging from 2 to 14 atoms), we accurately predict ground state energies, pair density functions, and two-body contact parameters $C^{(N)}_2$ related to weak unitarity. The results obtained via the variational Monte Carlo method exhibit remarkable agreement with previous studies using the diffusion Monte Carlo method, which is considered exact within its statistical uncertainties. This indicates the effectiveness of our neural network approach for investigating many-body systems governed by Bose-Einstein statistics. |
| first_indexed | 2024-03-08T09:22:08Z |
| format | Article |
| id | doaj.art-710efa4e373e45e0b5bb25b308bda1ee |
| institution | Directory Open Access Journal |
| issn | 2521-327X |
| language | English |
| last_indexed | 2024-03-08T09:22:08Z |
| publishDate | 2023-12-01 |
| publisher | Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften |
| record_format | Article |
| series | Quantum |
| spelling | doaj.art-710efa4e373e45e0b5bb25b308bda1ee2024-01-31T11:35:43ZengVerein zur Förderung des Open Access Publizierens in den QuantenwissenschaftenQuantum2521-327X2023-12-017120910.22331/q-2023-12-18-120910.22331/q-2023-12-18-1209Synergy between deep neural networks and the variational Monte Carlo method for small $^4He_N$ clustersWilliam FreitasS. A. VitielloWe introduce a neural network-based approach for modeling wave functions that satisfy Bose-Einstein statistics. Applying this model to small $^4He_N$ clusters (with N ranging from 2 to 14 atoms), we accurately predict ground state energies, pair density functions, and two-body contact parameters $C^{(N)}_2$ related to weak unitarity. The results obtained via the variational Monte Carlo method exhibit remarkable agreement with previous studies using the diffusion Monte Carlo method, which is considered exact within its statistical uncertainties. This indicates the effectiveness of our neural network approach for investigating many-body systems governed by Bose-Einstein statistics.https://quantum-journal.org/papers/q-2023-12-18-1209/pdf/ |
| spellingShingle | William Freitas S. A. Vitiello Synergy between deep neural networks and the variational Monte Carlo method for small $^4He_N$ clusters Quantum |
| title | Synergy between deep neural networks and the variational Monte Carlo method for small $^4He_N$ clusters |
| title_full | Synergy between deep neural networks and the variational Monte Carlo method for small $^4He_N$ clusters |
| title_fullStr | Synergy between deep neural networks and the variational Monte Carlo method for small $^4He_N$ clusters |
| title_full_unstemmed | Synergy between deep neural networks and the variational Monte Carlo method for small $^4He_N$ clusters |
| title_short | Synergy between deep neural networks and the variational Monte Carlo method for small $^4He_N$ clusters |
| title_sort | synergy between deep neural networks and the variational monte carlo method for small 4he n clusters |
| url | https://quantum-journal.org/papers/q-2023-12-18-1209/pdf/ |
| work_keys_str_mv | AT williamfreitas synergybetweendeepneuralnetworksandthevariationalmontecarlomethodforsmall4henclusters AT savitiello synergybetweendeepneuralnetworksandthevariationalmontecarlomethodforsmall4henclusters |