2.1k citations · 6.4k across the 21 of their papers we have counts for
21 papers
Ferromagnetic helical nodal line and Kane-Mele spin-orbit coupling in kagome metal Fe3Sn2
Shiang Fang, Linda Ye, Madhav Prasad Ghimire +8
The two-dimensional kagome lattice hosts Dirac fermions at its Brillouin zone corners K and K', analogous to the honeycomb lattice. In the density functional theory electronic stru…
In-Plane Ferroelectric Tunnel Junction
Huitao Shen, Junwei Liu, Kai Chang +1
The ferroelectric material is an important platform to realize non-volatile memories. So far, existing ferroelectric memory devices utilize out-of-plane polarization in ferroelectr…
Symmetry Enforced Self-Learning Monte Carlo Method Applied to the Holstein Model
Chuang Chen, Xiao Yan Xu, Junwei Liu +3
Self-learning Monte Carlo method (SLMC), using a trained effective model to guide Monte Carlo sampling processes, is a powerful general-purpose numerical method recently introduced…
Self-learning Monte Carlo with Deep Neural Networks
Huitao Shen, Junwei Liu, Liang Fu
Self-learning Monte Carlo (SLMC) method is a general algorithm to speedup MC simulations. Its efficiency has been demonstrated in various systems by introducing an effective model…
Massive Dirac fermions in a ferromagnetic kagome metal
Linda Ye, Mingu Kang, Junwei Liu +10
The kagome lattice is a two-dimensional network of corner-sharing triangles known as a platform for exotic quantum magnetic states. Theoretical work has predicted that the kagome l…
Self-Learning Monte Carlo Method: Continuous-Time Algorithm
Yuki Nagai, Huitao Shen, Yang Qi +2
The recently-introduced self-learning Monte Carlo method is a general-purpose numerical method that speeds up Monte Carlo simulations by training an effective model to propose unco…