548 citations · 2.5k across the 30 of their papers we have counts for
30 papers
Observation of a 1/3 Magnetisation Plateau Phase as Evidence for the Kitaev Interaction in a Honeycomb-Lattice Antiferromagnet
Yanyan Shangguan, Song Bao, Zhao-Yang Dong +24
Fractional magnetisation plateaus, in which the magnetisation is pinned at a fraction of its saturated value within a range of external magnetic field, are spectacular macroscopic…
Controllable spin splitting in 2D Ferroelectric few-layer ${$γ}$-GeSe
Shuyi Shi, Kuan-Rong Hao, Xing-Yu Ma +2
-GeSe is a new type of layered bulk material that was recently successfully synthesized. By means of density functional theory first-principles calculations, we systematically s…
Plaquette Singlet Transition, Magnetic Barocaloric Effect, and Spin Supersolidity in the Shastry-Sutherland Model
Junsen Wang, Han Li, Ning Xi +4
Inspired by recent experimental measurements [Guo \textit{et al.}, Phys. Rev. Lett.~\textbf{124}, 206602 (2020); Jiménez \textit{et al.}, Nature \textbf{592}, 370 (2021)] on frustr…
The atlas of ferroicity in two dimensional MGeX3 family: room-temperature ferromagnetic half metals and unexpected ferroelectricity and ferroelasticity
Kuan-Rong Hao, Xing-Yu Ma, Hou-Yi Lyu +3
Two-dimensional (2D) ferromagnetic and ferroelectric materials attract unprecedented attention due to the spontaneous-symmetry-breaking induced novel properties and multifarious po…
Calculated Effects of Vacancy and Ti-doping in 2D Janus MoSSe for Photocatalysis
Yi-Ming Zhao, Pengju Ren, Xing-Yu Ma +3
Two-dimensional (2D) Janus transitional metal dichalcogenides (TMDCs) have great potential for photocatalytic water splitting due to their novel properties induced by the unique ou…
High-Efficient ab initio Bayesian Active Learning Method and Applications in Prediction of Two-dimensional Functional Materials
Xing-Yu Ma, Hou-Yi Lyu, Kuan-Rong Hao +3
Beyond the conventional trial-and-error method, machine learning offers a great opportunity to accelerate the discovery of functional materials, but still often suffers from diffic…