Efficient Solutions of Fermionic Systems using Artificial Neural Networks
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866915096274403328 |
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| author | Nordhagen, Even M. Kim, Jane M. Fore, Bryce Lovato, Alessandro Hjorth-Jensen, Morten |
| author_facet | Nordhagen, Even M. Kim, Jane M. Fore, Bryce Lovato, Alessandro Hjorth-Jensen, Morten |
| contents | We discuss differences and similarities between variational Monte Carlo approaches that use conventional and artificial neural network parameterizations of the ground-state wave function for systems of fermions. We focus on a relatively shallow neural-network architectures, the so called restricted Boltzmann machine, and discuss unsupervised learning algorithms that are suitable to model complicated many-body correlations. We analyze the strengths and weaknesses of conventional and neural-network wave functions by solving various circular quantum-dots systems. Results for up to 90 electrons are presented and particular emphasis is placed on how to efficiently implement these methods on homogeneous and heterogeneous high-performance computing facilities. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2210_00365 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Efficient Solutions of Fermionic Systems using Artificial Neural Networks Nordhagen, Even M. Kim, Jane M. Fore, Bryce Lovato, Alessandro Hjorth-Jensen, Morten Mesoscale and Nanoscale Physics Nuclear Theory We discuss differences and similarities between variational Monte Carlo approaches that use conventional and artificial neural network parameterizations of the ground-state wave function for systems of fermions. We focus on a relatively shallow neural-network architectures, the so called restricted Boltzmann machine, and discuss unsupervised learning algorithms that are suitable to model complicated many-body correlations. We analyze the strengths and weaknesses of conventional and neural-network wave functions by solving various circular quantum-dots systems. Results for up to 90 electrons are presented and particular emphasis is placed on how to efficiently implement these methods on homogeneous and heterogeneous high-performance computing facilities. |
| title | Efficient Solutions of Fermionic Systems using Artificial Neural Networks |
| topic | Mesoscale and Nanoscale Physics Nuclear Theory |
| url | https://arxiv.org/abs/2210.00365 |