activity
20182026
most citedExploring T-carbon for Energy Applications

45 citations · 48 across the 4 of their papers we have counts for

collaborators

8 papers

cond-mat.supr-con2026

Discovery of a nonsymmorphic superconductor with spontaneous rotational symmetry breaking and nontrivial zero modes

Hui Guo, Zhixuan Li, Senhao Lv +13

Topological superconductivity has attracted great interest due to its fundamental significance for realizing Majorana quasiparticles and fault-tolerant quantum computation. Nonsymm…

cond-mat.mtrl-sci20241 cited

Topological skyrmions in monolayer multiferroic MoPtGe2S6

Zuxin Fu, Kuanrong Hao, Min Guo +5

Two-dimensional (2D) multiferroic materials with coexisting ferroelectricity and ferromagnetism have garnered substantial attention for their intriguing physical properties and div…

cond-mat.mtrl-sci20212 cited

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…

cond-mat.mtrl-sci2021

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…

cond-mat.mtrl-sci2020

Voting Data-Driven Regression Learning for Discovery of Functional Materials and Applications to Two-Dimensional Ferroelectric Materials

Xing-Yu Ma, Hou-Yi Lyu, Xue-Juan Dong +4

Regression machine learning is widely applied to predict various materials. However, insufficient materials data usually leads to a poor performance. Here, we develop a new voting…

cond-mat.mtrl-sci2020

Large Family of Two-Dimensional Ferroelectric Metals Discovered via Machine Learning

Xing-Yu Ma, Hou-Yi Lyu, Kuan-Rong Hao +4

Ferroelectricity and metallicity are usually believed not to coexist because conducting electrons would screen out static internal electric fields. In 1965, Anderson and Blount pro…