Machine-learned phase diagrams of generalized Kitaev honeycomb magnets

Rao, Nihal; Liu, Ke; Machaczek, Marc und Pollet, Lode (2021): Machine-learned phase diagrams of generalized Kitaev honeycomb magnets. In: Physical Review Research, Bd. 3, Nr. 3, 33223

Abstract

We use a recently developed interpretable and unsupervised machine-learning method, the tensorial kernel support vector machine, to investigate the low-temperature classical phase diagram of a generalized HeisenbergKitaev-Gamma (J-K-F) model on a honeycomb lattice. Aside from reproducing phases reported by previous quantum and classical studies, our machine finds a hitherto missed nested zigzag-stripy order and establishes the robustness of a recently identified modulated S-3 x Z(3) phase, which emerges through the competition between the Kitaev and Gamma spin liquids, against Heisenberg interactions. The results imply that, in the restricted parameter space spanned by the three primary exchange interactions-J, K, and Gamma, the representative Kitaev material alpha-RuCl3 lies close to the boundaries of several phases, including a simple ferromagnet, the unconventional S-3 x Z(3), and nested zigzag-stripy magnets. A zigzag order is stabilized by a finite Gamma' and/or J(3) term, whereas the four magnetic orders may compete in particular if Gamma' is antiferromagnetic.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Physik
• Themengebiete: 500 Naturwissenschaften und Mathematik > 530 Physik
• Sprache: Englisch