75 citations · 231 across the 8 of their papers we have counts for
8 papers
The view of TK-SVM on the phase hierarchy in the classical kagome Heisenberg antiferromagnet
Jonas Greitemann, Ke Liu, Lode Pollet
We illustrate how the tensorial kernel support vector machine (TK-SVM) can probe the hidden multipolar orders and emergent local constraint in the classical kagome Heisenberg antif…
Revealing the Phase Diagram of Kitaev Materials by Machine Learning: Cooperation and Competition between Spin Liquids
Ke Liu, Nicolas Sadoune, Nihal Rao +2
Kitaev materials are promising materials for hosting quantum spin liquids and investigating the interplay of topological and symmetry-breaking phases. We use an unsupervised and in…
Identification of hidden order and emergent constraints in frustrated magnets using tensorial kernel methods
Jonas Greitemann, Ke Liu, Ludovic D. C. Jaubert +3
Machine-learning techniques have proved successful in identifying ordered phases of matter. However, it remains an open question how far they can contribute to the understanding of…
Learning multiple order parameters with interpretable machines
Ke Liu, Jonas Greitemann, Lode Pollet
Machine-learning techniques are evolving into a subsidiary tool for studying phase transitions in many-body systems. However, most studies are tied to situations involving only one…
Probing hidden spin order with interpretable machine learning
Jonas Greitemann, Ke Liu, Lode Pollet
The search of unconventional magnetic and nonmagnetic states is a major topic in the study of frustrated magnetism. Canonical examples of those states include various spin liquids…
Lecture notes on Diagrammatic Monte Carlo for the Fröhlich polaron
Jonas Greitemann, Lode Pollet
These notes are intended as a detailed discussion on how to implement the diagrammatic Monte Carlo method for a physical system which is technically simple and where it works extre…