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Lichuan Wang

Publications and source records attributed to Lichuan Wang.

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Spin-Orbital Intertwined Topological Superconductivity in a Class of Correlated Noncentrosymmetric Materials

In this study, we propose an alternative route to achieving topological superconductivity (TSC). Our approach applies to a new class of correlated noncentrosymmetric materials that host two spin-split Fermi surfaces with identical spin textures due to a spin-orbital intertwined effect. Incorporating multi-orbital repulsive Hubbard interactions, we calculate the superconducting pairings of a minimal two-orbital effective model within a spin-fluctuation-mediated superconductivity framework. We find that, depending on the effective Rashba spin-orbit coupling (RSOC) strength and filling level, the Hubbard interaction can drive the leading pairing symmetry into the $A_1(S_{\pm})$, $B_1$, $B_2$ or $B_2(d_{\pm})$ irreducible representations (IRs) of the $C_{4v}$ point group. Notably, the $A_1(S_{\pm})$ pairing gives rise to a fully gapped TSC characterized by a $Z_2$ invariant, while the $B_2(d_{\pm})$ pairing results in a nodal TSC. Our analysis reveals that the fully gapped TSC is predominated by spin-singlet regardless of the presence of the spin-triplet components. This distinguishes our model from noncentrosymmetric materials with conventional Rashba-split band structures, where TSC typically emerges near the van Hove singularity and is primarily driven by $p$-wave or $f$-wave spin-triplet pairing. These features enhances its experimental accessibility, and we discuss potential experimental systems for its realization.

cond-mat.supr-con

Superconductivity in Two-Dimensional Systems with Unconventional Rashba Bands

In two-dimensional system with Rashba spin-orbit coupling, it is well-known that superconductivity can have mixed spin-singlet and -triplet parity, and the $\boldsymbol{d}$-vector of spin-triplet pairing is parallel to $\boldsymbol{g}$-vector of Rashba spin-orbit coupling. Here, we propose a model to describe a two-dimensional system with unconventional Rashba bands and study its superconductivity. We show that the $\boldsymbol{d}$-vector of spin-triplet pairing can be either parallel or perpendicular to $\boldsymbol{g}$-vector of Rashba spin-orbit coupling depending on the different pairing interaction. We also propose a junction to generate tunneling current depending on the direction of $\boldsymbol{d}$-vector. It provides a detectable evidence to distinguish these two different but very similar pairing channels. Furthermore, we find this model can give arise to a subleading spin-singlet chiral $p$-wave topological superconducting state. More significantly, we find that such unconventional Rashba bands and unconventional superconudcting pairings can be realized on surface of some superconducting topological materials, such as trigonal layered PtBi$_{2}$.

cond-mat.supr-con

MNN: A Universal and Efficient Inference Engine

Deploying deep learning models on mobile devices draws more and more attention recently. However, designing an efficient inference engine on devices is under the great challenges of model compatibility, device diversity, and resource limitation. To deal with these challenges, we propose Mobile Neural Network (MNN), a universal and efficient inference engine tailored to mobile applications. In this paper, the contributions of MNN include: (1) presenting a mechanism called pre-inference that manages to conduct runtime optimization; (2)deliveringthorough kernel optimization on operators to achieve optimal computation performance; (3) introducing backend abstraction module which enables hybrid scheduling and keeps the engine lightweight. Extensive benchmark experiments demonstrate that MNN performs favorably against other popular lightweight deep learning frameworks. MNN is available to public at: https://github.com/alibaba/MNN.

cs.CV