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Found 5 papers, 5 papers with code
Machine-Learned Phase Diagrams of Generalized Kitaev Honeycomb Magnets
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
Kitaev materials are promising materials for hosting quantum spin liquids and investigating the interplay of topological and symmetry-breaking phases.
Identification of hidden order and emergent constraints in frustrated magnets using tensorial kernel methods
Machine-learning techniques have proved successful in identifying ordered phases of matter.
Strongly Correlated Electrons Statistical Mechanics
Learning multiple order parameters with interpretable machines
Furthermore, we discuss an intrinsic parameter of SVM, the bias, which allows for a special interpretation in the classification of phases, and its utility in diagnosing the existence of phase transitions.
Statistical Mechanics Strongly Correlated Electrons Computational Physics
Probing hidden spin order with interpretable machine learning
The search of unconventional magnetic and nonmagnetic states is a major topic in the study of frustrated magnetism.
