Learning spin liquids on a honeycomb lattice with artificial neural networks

Abstract Machine learning methods provide a new perspective on the study of many-body system in condensed matter physics and there is only limited understanding of their representational properties and limitations in quantum spin liquid systems. In this work, we investigate the ability of the machin...

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Auteurs principaux: Chang-Xiao Li, Sheng Yang, Jing-Bo Xu
Format: article
Langue:EN
Publié: Nature Portfolio 2021
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R
Q
Accès en ligne:https://doaj.org/article/d62a9389027543848943424de4bd2cde
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