Synergy between deep neural networks and the variational Monte Carlo method for small $^4He_N$ clusters

• URL: http://arxiv.org/abs/2302.00599v3
• Date: Wed, 13 Dec 2023 21:11:23 GMT
• Title: Synergy between deep neural networks and the variational Monte Carlo method for small $^4He_N$ clusters
• Authors: William Freitas and S. A. Vitiello
• Abstract summary: We introduce a neural network-based approach for modeling wave functions that satisfy Bose-Einstein statistics. Applying this model to small $4He_N$ clusters, we accurately predict ground state energies, pair density functions, and two-body contact parameters.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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