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 machine learning method based on the restricted Boltzmann machine in capturing physical quantities including the ground-state energy, spin-structure factor, magnetization, quantum coherence, and multipartite entanglement in the two-dimensional ferromagnetic spin liquids on a honeycomb lattice. It is found that the restricted Boltzmann machine can encode the many-body wavefunction quite well by reproducing accurate ground-state energy and structure factor. Further investigation on the behavior of multipartite e

SUBMITTER: Li CX 

PROVIDER: S-EPMC8371168 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature