Spin-1/2 kagome Heisenberg antiferromagnet: Machine learning discovery of the spinon pair density wave ground state
Tanja Duric, Jia Hui Chung, Bo Yang, Pinaki Sengupta

TL;DR
This study uses advanced machine learning techniques to investigate the ground state of the spin-1/2 kagome antiferromagnet, revealing a novel spinon pair density wave state that challenges previous predictions.
Contribution
It introduces a group equivariant convolutional neural network approach combined with variational Monte Carlo to accurately determine the ground state of frustrated spin systems.
Findings
Identifies a spinon pair density wave as the ground state.
Finds the state does not break time-reversal or lattice symmetries.
Achieves lower energy results than previous methods.
Abstract
Spin-1/2 kagome antiferromagnet (AFM) is one of the most studied models in frustrated magnetism since it is a promising candidate to host exotic spin liquid states. However, despite numerous studies using both analytical and numerical approaches, the nature of the ground state and low-energy excitations in this system remain elusive. This is related to the difficulty in determining the spin gap in various calculations. We present the results of our investigation of the Kagome AFM using the recently developed group equivariant convolutional neural networks, a novel machine learning technique for studying strongly frustrated models. The approach, combined with variational Monte Carlo, introduces significant improvement of the achievable results accuracy for frustrated spin systems in comparison with approaches based on other neural network architectures. Contrary to the results obtained…
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Taxonomy
TopicsAdvanced Condensed Matter Physics · Physics of Superconductivity and Magnetism · Theoretical and Computational Physics
