Dirac-type nodal spin liquid revealed by machine learning
Yusuke Nomura, Masatoshi Imada
Abstract
Pursuing fractionalized particles that do not bear properties of conventional bare particles such as electrons or magnons is a challenge in physics. Here we show that machine-learning methods for quantum many-body systems reveal the existence of a quantum spin liquid state with fractionalized spinons in spin-1/2 frustrated Heisenberg model convincingly, if it is combined with the state-of-the-art computational schemes known as the correlation ratio and level spectroscopy methods. The spin excitation spectra signal the emergence of gapless fractionalized spin-1/2 Dirac-type spinons in the distinctive quantum spin liquid phase. Unexplored critical behavior with coexisting power-law-decaying antiferromagnetic and dimer correlations emerges as well. The isomorph of excitations with the cuprate d-wave superconductors revealed here implies tight connection between the present spin liquid and superconductivity. This achievement manifests the power of machine learning for grand challenges in quantum many-body physics.