activity
20182026
most citedMagnetically Controllable Topological Quantum Phase Transitions in Antiferromagnetic Topological Insulator MnBiTe

142 citations · 466 across the 14 of their papers we have counts for

collaborators

13 papers

cond-mat.mtrl-sci2026

Manipulating Charge Distribution in Moiré Superlattices by Light

Ruiping Guo, Haowei Chen, Wenhui Duan +2

In ordinary solids, nonlinear optical responses are typically studied in terms of unit-cell averages due to the angström-scale lattice constants. In contrast, moiré superlattices,…

cond-mat.str-el2024★ 1 cited

Magnetorotons in Moiré Fractional Chern Insulators

Xiaoyang Shen, Chonghao Wang, Xiaodong Hu +5

The discovery of fractional Chern insulators (FCIs) unlocks exciting opportunities to explore emergent physical excitations arising from topological and geometric effects in novel…

cond-mat.str-el2024

Fractional Chern insulators in moiré flat bands with high Chern numbers

Chonghao Wang, Xiaoyang Shen, Ruiping Guo +3

Recent discoveries of zero-field fractional Chern insulators in moiré materials have attracted intensive research interests. However, most current theoretical and experimental atte…

cond-mat.str-el2024

Stabilizing fractional Chern insulators via exchange interaction in moiré systems

Xiaoyang Shen, Chonghao Wang, Ruiping Guo +3

Recent experimental discovery of fractional Chern insulator in moiré Chern band in twisted transition metal dichalocogenide homobilayers has sparked intensive interest in exploring…

cond-mat.supr-con2024★ 1 cited

Enhancement of Ising superconductivity in monolayer NbSe via surface fluorination

Jizheng Wu, Wujun Shi, Chong Wang +3

Recently discovered Ising superconductors have garnered considerable interest due to their anomalously large in-plane upper critical fields (). However, the requisite stron…

cs.LG2021★ 1 cited

Heterogeneous relational message passing networks for molecular dynamics simulations

Zun Wang, Chong Wang, Sibo Zhao +5

With many frameworks based on message passing neural networks proposed to predict molecular and bulk properties, machine learning methods have tremendously shifted the paradigms of…