Fermionic neural-network states for ab-initio electronic structure
Despite the importance of neural-network quantum states, representing fermionic matter is yet to be fully achieved. Here the authors map fermionic degrees of freedom to spin ones and use neural-networks to perform electronic structure calculations on model diatomic molecules to achieve chemical accu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2020-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-15724-9 |
| _version_ | 1818717120380272640 |
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| author | Kenny Choo Antonio Mezzacapo Giuseppe Carleo |
| author_facet | Kenny Choo Antonio Mezzacapo Giuseppe Carleo |
| author_sort | Kenny Choo |
| collection | DOAJ |
| description | Despite the importance of neural-network quantum states, representing fermionic matter is yet to be fully achieved. Here the authors map fermionic degrees of freedom to spin ones and use neural-networks to perform electronic structure calculations on model diatomic molecules to achieve chemical accuracy. |
| first_indexed | 2024-12-17T19:30:06Z |
| format | Article |
| id | doaj.art-33bdf8234a854173ac0adada5147c2a5 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-17T19:30:06Z |
| publishDate | 2020-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-33bdf8234a854173ac0adada5147c2a52022-12-21T21:35:16ZengNature PortfolioNature Communications2041-17232020-05-011111710.1038/s41467-020-15724-9Fermionic neural-network states for ab-initio electronic structureKenny Choo0Antonio Mezzacapo1Giuseppe Carleo2Department of Physics, University of ZurichIBM Thomas J. Watson Research CenterCenter for Computational Quantum Physics, Flatiron InstituteDespite the importance of neural-network quantum states, representing fermionic matter is yet to be fully achieved. Here the authors map fermionic degrees of freedom to spin ones and use neural-networks to perform electronic structure calculations on model diatomic molecules to achieve chemical accuracy.https://doi.org/10.1038/s41467-020-15724-9 |
| spellingShingle | Kenny Choo Antonio Mezzacapo Giuseppe Carleo Fermionic neural-network states for ab-initio electronic structure Nature Communications |
| title | Fermionic neural-network states for ab-initio electronic structure |
| title_full | Fermionic neural-network states for ab-initio electronic structure |
| title_fullStr | Fermionic neural-network states for ab-initio electronic structure |
| title_full_unstemmed | Fermionic neural-network states for ab-initio electronic structure |
| title_short | Fermionic neural-network states for ab-initio electronic structure |
| title_sort | fermionic neural network states for ab initio electronic structure |
| url | https://doi.org/10.1038/s41467-020-15724-9 |
| work_keys_str_mv | AT kennychoo fermionicneuralnetworkstatesforabinitioelectronicstructure AT antoniomezzacapo fermionicneuralnetworkstatesforabinitioelectronicstructure AT giuseppecarleo fermionicneuralnetworkstatesforabinitioelectronicstructure |