Heuristic machinery for thermodynamic studies of SU(N) fermions with neural networks

The detection of the effects of spin symmetry in momentum distribution of an SU(N)-symmetric Fermi gas has remained challenging. Here, the authors use supervised machine learning to connect the spin multiplicity to thermodynamic quantities associated with different parts of the momentum distribution...

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Autores principales:	Entong Zhao, Jeongwon Lee, Chengdong He, Zejian Ren, Elnur Hajiyev, Junwei Liu, Gyu-Boong Jo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/66743b77dd114252857f7fdcad5029de
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id	oai:doaj.org-article:66743b77dd114252857f7fdcad5029de
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spelling	oai:doaj.org-article:66743b77dd114252857f7fdcad5029de2021-12-02T14:23:03ZHeuristic machinery for thermodynamic studies of SU(N) fermions with neural networks10.1038/s41467-021-22270-52041-1723https://doaj.org/article/66743b77dd114252857f7fdcad5029de2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-22270-5https://doaj.org/toc/2041-1723The detection of the effects of spin symmetry in momentum distribution of an SU(N)-symmetric Fermi gas has remained challenging. Here, the authors use supervised machine learning to connect the spin multiplicity to thermodynamic quantities associated with different parts of the momentum distribution.Entong ZhaoJeongwon LeeChengdong HeZejian RenElnur HajiyevJunwei LiuGyu-Boong JoNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Entong Zhao Jeongwon Lee Chengdong He Zejian Ren Elnur Hajiyev Junwei Liu Gyu-Boong Jo Heuristic machinery for thermodynamic studies of SU(N) fermions with neural networks
description	The detection of the effects of spin symmetry in momentum distribution of an SU(N)-symmetric Fermi gas has remained challenging. Here, the authors use supervised machine learning to connect the spin multiplicity to thermodynamic quantities associated with different parts of the momentum distribution.
format	article
author	Entong Zhao Jeongwon Lee Chengdong He Zejian Ren Elnur Hajiyev Junwei Liu Gyu-Boong Jo
author_facet	Entong Zhao Jeongwon Lee Chengdong He Zejian Ren Elnur Hajiyev Junwei Liu Gyu-Boong Jo
author_sort	Entong Zhao
title	Heuristic machinery for thermodynamic studies of SU(N) fermions with neural networks
title_short	Heuristic machinery for thermodynamic studies of SU(N) fermions with neural networks
title_full	Heuristic machinery for thermodynamic studies of SU(N) fermions with neural networks
title_fullStr	Heuristic machinery for thermodynamic studies of SU(N) fermions with neural networks
title_full_unstemmed	Heuristic machinery for thermodynamic studies of SU(N) fermions with neural networks
title_sort	heuristic machinery for thermodynamic studies of su(n) fermions with neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/66743b77dd114252857f7fdcad5029de
work_keys_str_mv	AT entongzhao heuristicmachineryforthermodynamicstudiesofsunfermionswithneuralnetworks AT jeongwonlee heuristicmachineryforthermodynamicstudiesofsunfermionswithneuralnetworks AT chengdonghe heuristicmachineryforthermodynamicstudiesofsunfermionswithneuralnetworks AT zejianren heuristicmachineryforthermodynamicstudiesofsunfermionswithneuralnetworks AT elnurhajiyev heuristicmachineryforthermodynamicstudiesofsunfermionswithneuralnetworks AT junweiliu heuristicmachineryforthermodynamicstudiesofsunfermionswithneuralnetworks AT gyuboongjo heuristicmachineryforthermodynamicstudiesofsunfermionswithneuralnetworks
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Heuristic machinery for thermodynamic studies of SU(N) fermions with neural networks

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