Q
Quan Zhou
Researcher at Stanford University
Publications - 8
Citations - 1905
Quan Zhou is an academic researcher from Stanford University. The author has contributed to research in topics: Fermion & Topological insulator. The author has an hindex of 7, co-authored 7 publications receiving 1624 citations.
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Journal ArticleDOI
Chiral Majorana fermion modes in a quantum anomalous Hall insulator-superconductor structure.
Qing Lin He,Lei Pan,Alexander Stern,Edward C. Burks,Xiaoyu Che,Gen Yin,Jing Wang,Jing Wang,Biao Lian,Quan Zhou,Eun Sang Choi,Koichi Murata,Xufeng Kou,Xufeng Kou,Zhijie Chen,Tianxiao Nie,Qiming Shao,Yabin Fan,Shou-Cheng Zhang,Kai Liu,Jing Xia,Kang L. Wang,Kang L. Wang +22 more
TL;DR: In this paper, the authors report transport measurements that suggest the existence of one-dimensional chiral Majorana fermion modes in the hybrid system of a quantum anomalous Hall insulator thin film coupled with a superconductor.
Journal ArticleDOI
Chiral Majorana edge state in a quantum anomalous Hall insulator-superconductor structure
Qing Lin He,Lei Pan,Alexander Stern,Edward C. Burks,Xiaoyu Che,Gen Yin,Jing Wang,Biao Lian,Quan Zhou,Eun Sang Choi,Koichi Murata,Xufeng Kou,Tianxiao Nie,Qiming Shao,Yabin Fan,Shou-Cheng Zhang,Kai Liu,Jing Xia,Kang L. Wang +18 more
TL;DR: In this article, a collection of Majorana fermions living in a one-dimensional transport channel at the boundary of a superconducting quantum anomalous Hall insulator thin film is demonstrated.
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Multiple types of topological fermions in transition metal silicides
TL;DR: By using the ab initio density functional theory, it is shown that these unconventional quasiparticles coexist with type-I and type-II Weyl fermions in a family of transition metal silicides, including CoSi, Rh Si, RhGe, and CoGe, when spin-orbit coupling is considered.
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Dirac fermions in an antiferromagnetic semimetal
TL;DR: In this article, it was shown that Dirac fermions can exist in one type of antiferromagnetic system, where both and are broken but their combination is respected.
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Learning atoms for materials discovery.
TL;DR: The unsupervised machines (Atom2Vec) can learn the basic properties of atoms by themselves from the extensive database of known compounds and materials, represented in terms of high-dimensional vectors, and clustering of atoms in vector space classifies them into meaningful groups consistent with human knowledge.