arXiv subjects

Xinjiang Wang

Publications and source records attributed to Xinjiang Wang.

41 records · Page 3Linked to original sources

arXiv2018

Interlayer Coupling in Two-Dimensional Semiconductor Materials

Two-dimensional (2D) graphene-like layered semiconductors provide a new platform for materials research because of their unique mechanical, electronic and optical attributes. Their in-plane covalent bonding and dangling-bond-free surface allow them to assemble various van der Waals heterostructures (vdWHSs) with sharply atomic interfaces that are not limited by lattice matching and material compatibility. Interlayer coupling, as a ubiquitous phenomenon residing among 2D materials (2DMs) systems, controls a thin layer exfoliation process and the assembly of vdWHSs and behaves with a unique degree of freedom for engineering the properties of 2DMs. Interlayer coupling provides an opportunity to observe new physics and provides a novel strategy to modulate the electronic and optoelectronic properties of materials for practical device applications. We herein review recent progress in the exploration of interlayer coupling in 2D semiconducting vdWHSs for potential applications in electronics and optoelectronics.

cond-mat.mtrl-sci↗

arXiv2018

Batch Kalman Normalization: Towards Training Deep Neural Networks with Micro-Batches

As an indispensable component, Batch Normalization (BN) has successfully improved the training of deep neural networks (DNNs) with mini-batches, by normalizing the distribution of the internal representation for each hidden layer. However, the effectiveness of BN would diminish with scenario of micro-batch (e.g., less than 10 samples in a mini-batch), since the estimated statistics in a mini-batch are not reliable with insufficient samples. In this paper, we present a novel normalization method, called Batch Kalman Normalization (BKN), for improving and accelerating the training of DNNs, particularly under the context of micro-batches. Specifically, unlike the existing solutions treating each hidden layer as an isolated system, BKN treats all the layers in a network as a whole system, and estimates the statistics of a certain layer by considering the distributions of all its preceding layers, mimicking the merits of Kalman Filtering. BKN has two appealing properties. First, it enables more stable training and faster convergence compared to previous works. Second, training DNNs using BKN performs substantially better than those using BN and its variants, especially when very small mini-batches are presented. On the image classification benchmark of ImageNet, using BKN powered networks we improve upon the best-published model-zoo results: reaching 74.0% top-1 val accuracy for InceptionV2. More importantly, using BKN achieves the comparable accuracy with extremely smaller batch size, such as 64 times smaller on CIFAR-10/100 and 8 times smaller on ImageNet.

cs.CV↗

arXiv2017

Further improvement of lattice thermal conductivity from bulk crystalline to 1-D-chain polyethylene: A high-yet-finite thermal conductivity using first-principles calculation

We calculate the thermal conductivity (\k{appa}) of both bulk crystalline and single-chain polyethylene (PE) using the first-principles-based anharmonic lattice dynamics. Despite its low \k{appa} in amorphous state, the predicted bulk crystal has high axial \k{appa} (237 W/m-K) at room temperature. The much lower measured \k{appa} is attributed to the small size of nanocrystallites (~10 nm) in synthesized semi-crystalline PE. For the 1-D chain, the predicted \k{appa} is much larger and yet finite (1400 W/m-K at room temperature). The reduction of scattering phase space caused by the diminished interchain van der Waals interactions explains this larger \k{appa}. It is also found that the transverse phonon branches with quadratic dispersion make minor contribution to this, due to their vanishing group velocity in the long-wavelength limit. Moreover, the low-frequency bending and twisting phonon modes are strongly coupled and dominate anharmonic phonon scatterings, leading to the finite \k{appa}. The predicted high \k{appa} of bulk and chain PE crystals enable polymer usage in thermal management and the above phonon scatterings provide guide for their nano-designs.

cond-mat.mes-hall↗

arXiv2015

First-principles study of anisotropic thermoelectric transport properties of IV-VI semiconductor compounds SnSe and SnS

We conduct comprehensive investigations of both thermal and electrical transport properties of SnSe and SnS using first-principles calculations combined with the Boltzmann transport theory. Due to the distinct layered lattice structure, SnSe and SnS exhibit similarly anisotropic thermal and electrical behaviors. The cross-plane lattice thermal conductivity $κ_{L}$ is 40-60% lower than the in-plane values. Extremely low $κ_{L}$ is found for both materials because of high anharmonicity. It is suggested that nanostructuring would be difficult to further decrease $κ_{L}$ because of the short mean free paths of dominant phonon modes (1-30 nm at 300 K) while alloying would be efficient in reducing $κ_{L}$ considering that the relative $κ_{L}$ contribution ($\sim$ 65%) of optical phonons is remarkably large. On the electrical side, the anisotropic electrical conductivities are mainly due to the different effective masses of holes and electrons along the $a$, $b$ and $c$ axes. This leads to the highest optimal $ZT$ values along the $b$ axis and lowest ones along the $a$ axis in both $p$-type materials. However, the $n$-type ones exhibit the highest $ZT$s along the $a$ axis due to the enhancement of power factor when the chemical potential gradually approaches the secondary band valley that causes significant increase in electron mobility and density of states. SnSe exhibits larger optimal $ZT$s compared with SnS in both $p$-type and $n$-type materials. For both materials, the peak $ZT$s of $n$-type materials are much higher than those of $p$-type ones along the same direction. The predicted highest $ZT$ values at 750 K are 1.0 in SnSe and 0.6 in SnS along the $b$ axis for the $p$-type doping while those for the $n$-type doping reach 2.7 in SnSe and 1.5 in SnS along the $a$ axis, rendering them among the best bulk thermoelectric materials for large-scale applications.

cond-mat.mtrl-sci↗

arXiv2015

Thermal conductivity of graphene mediated by strain and size

Based on first-principles calculations and full iterative solution of the linearized Boltzmann-Peierls transport equation for phonons within three-phonon scattering framework, we characterize the lattice thermal conductivities $κ$ of strained and unstrained graphene. We find $κ$ converges to 5450 W/m-K for infinite unstrained graphene, while $κ$ diverges for strained graphene with increasing system size at room temperature. The different $κ$ behaviors for these systems are further validated mathematically through phonon lifetime analysis. Flexural acoustic phonons are the dominant heat carriers in both unstrained and strained graphene within the temperature considered. Ultralong mean free paths of flexural phonons contribute to finite size effects on $κ$ for samples as large as 8 cm at room temperature. The calculated size-dependent and temperature-dependent $κ$ for finite samples agree well with experimental data, demonstrating the ability of the present approach to predict $κ$ of larger graphene sample. Tensile strain hardens the flexural modes and increases their lifetimes, causing interesting dependence of $κ$ on sample size and strain due to the competition between boundary scattering and intrinsic phonon-phonon scattering. These findings shed light on the nature of thermal transport in two-dimensional materials and may guide predicting and engineering $κ$ of graphene by varying strain and size.

cond-mat.mtrl-sci↗