Machine-Learned Phase Diagrams of Generalized Kitaev Honeycomb Magnets

1 code implementation • 1 Feb 2021 • Nihal Rao, Ke Liu, Marc Machaczek, Lode Pollet

We use a recently developed interpretable and unsupervised machine-learning method, the tensorial kernel support vector machine (TK-SVM), to investigate the low-temperature classical phase diagram of a generalized Heisenberg-Kitaev-$\Gamma$ ($J$-$K$-$\Gamma$) model on a honeycomb lattice.

Revealing the Phase Diagram of Kitaev Materials by Machine Learning: Cooperation and Competition between Spin Liquids

1 code implementation • 29 Apr 2020 • Ke Liu, Nicolas Sadoune, Nihal Rao, Jonas Greitemann, Lode Pollet

Kitaev materials are promising materials for hosting quantum spin liquids and investigating the interplay of topological and symmetry-breaking phases.

BIG-bench Machine Learning Interpretable Machine Learning