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20182021
most citedSymmetry-adapted graph neural networks for constructing molecular dynamics force fields

1 citations · 1 across the 1 of their papers we have counts for

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5 papers

physics.comp-ph20211 cited

Symmetry-adapted graph neural networks for constructing molecular dynamics force fields

Zun Wang, Chong Wang, Sibo Zhao +4

Molecular dynamics is a powerful simulation tool to explore material properties. Most of the realistic material systems are too large to be simulated with first-principles molecula…

cond-mat.supr-con2019

In-plane ordering of O vacancies in a high-Tc cuprate superconductor with compressed Cu-O octahedrons: a first-principles cluster expansion study

Yunhao Li, Shiqiao Du, Zheng-Yu Weng +1

A recently discovered high-Tc cuprate superconductor Ba2CuO exhibits exceptional Jahn-Teller distortion, wherein the CuO6 octahedrons are compressed along the c axis. As a…

cond-mat.mtrl-sci2019

Berry Curvature Engineering by Gating Two-Dimensional Antiferromagnets

Shiqiao Du, Peizhe Tang, Jiaheng Li +4

Recent advances in tuning electronic, magnetic, and topological properties of two-dimensional (2D) magnets have opened a new frontier in the study of quantum physics and promised e…

cond-mat.mtrl-sci2019

Giant Enhancement of Solid Solubility in Monolayer BNC Alloys by Selective Orbital Coupling

Shiqiao Du, Jianfeng Wang, Lei Kang +2

Solid solubility (SS) is one of the most important features of alloys, which is usually difficult to be largely tuned in the entire alloy concentrations by external approaches. Som…

cond-mat.mtrl-sci2018

Intrinsic magnetic topological insulators in van der Waals layered MnBiTe-family materials

Jiaheng Li, Yang Li, Shiqiao Du +6

The interplay of magnetism and topology is a key research subject in condensed matter physics and material science, which offers great opportunities to explore emerging new physics…