arXiv subjects

Han Wang

Publications and source records attributed to Han Wang.

At least 505 records · Page 28Linked to original sources

arXiv2018

End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems

Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES for a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity.

physics.comp-ph↗

arXiv2018

Theoretical Analysis of Functionally Graded Piezoelectric Thick-walled Cylinder Subjected to Mechanical and Electric Loadings

In this paper, the theoretical analysis for a hollow thick-walled functionally graded piezoelectric cylinder subjected to electric and mechanical loads are developed. The cylinder consists of two materials (PZT4 and PVDF) and the volume fraction of PZT4 is given in the three variable parameters power law form. By using the Voigt method and the assumption of a uniform strain field within the two linear elastic constituents, the complex hypergeometric differential equation of the radial displacement is obtained. Then the solutions of the radial displacement, the stresses, and the electric potential are derived and solved. The method in this paper is more suitable for actual engineering gradient piezoelectric materials, and the volume fraction function can cover more complicated situations. Finally, the influence of the parameter n in volume fraction on the mechanical behaviors are investigated, and the difference between the circumferential and radial stresses is discussed to reduce the stress concentration in the functionally graded piezoelectric cylinder.

cond-mat.other↗

arXiv2018

Epitaxial Growth and Electrical Properties of VO2 on LSAT (111) substrate

We report the epitaxial growth and the electrical properties, especially the metal-to-insulator transition (MIT), of vanadium dioxide (VO2) thin films synthesized on LSAT (111) ([LaAlO3]0.3[Sr2AlTaO6]0.7) substrates by pulsed laser deposition. X-ray diffraction studies show that the epitaxial relationship between the VO2 thin films and LSAT substrate is given as VO2(020)||LSAT(111) and VO2[001]||LSAT[11-2]. We observed a sharp four orders of magnitude change in the longitudinal resistance for the VO2 thin films around the transition temperature. We also measured distinct Raman spectra below and above the transition point indicating a concomitant structural transition between the insulator and metallic phases, in agreement with past investigations.

cond-mat.mtrl-sci↗

arXiv2018

HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples

Adversarial machine learning in the context of image processing and related applications has received a large amount of attention. However, adversarial machine learning, especially adversarial deep learning, in the context of malware detection has received much less attention despite its apparent importance. In this paper, we present a framework for enhancing the robustness of Deep Neural Networks (DNNs) against adversarial malware samples, dubbed Hashing Transformation Deep Neural Networks} (HashTran-DNN). The core idea is to use hash functions with a certain locality-preserving property to transform samples to enhance the robustness of DNNs in malware classification. The framework further uses a Denoising Auto-Encoder (DAE) regularizer to reconstruct the hash representations of samples, making the resulting DNN classifiers capable of attaining the locality information in the latent space. We experiment with two concrete instantiations of the HashTran-DNN framework to classify Android malware. Experimental results show that four known attacks can render standard DNNs useless in classifying Android malware, that known defenses can at most defend three of the four attacks, and that HashTran-DNN can effectively defend against all of the four attacks.

cs.CR↗

arXiv2018

Adaptive coupling of a deep neural network potential to a classical force field

An adaptive modeling method (AMM) that couples a deep neural network potential and a classical force field is introduced to address the accuracy-efficiency dilemma faced by the molecular simulation community. The AMM simulated system is decomposed into three types of regions. The first type captures the important phenomena in the system and requires high accuracy, for which we use the Deep Potential Molecular Dynamics (DeePMD) model in this work. The DeePMD model is trained to accurately reproduce the statistical properties of the ab initio molecular dynamics. The second type does not require high accuracy and a classical force field is used to describe it in an efficient way. The third type is used for a smooth transition between the first and the second types of regions. By using a force interpolation scheme and imposing a thermodynamics force in the transition region, we make the DeePMD region embedded in the AMM simulated system as if it were embedded in a system that is fully described by the accurate potential. A representative example of the liquid water system is used to show the feasibility and promise of this method.

physics.chem-ph↗

arXiv2018

Electrical switching of perpendicular magnetization in L10 FePt single layer

Electrical manipulation of magnetization is essential for integration of magnetic functionalities such as magnetic memories and magnetic logic devices into electronic circuits. The current induced spin-orbit torque (SOT) in heavy metal/ferromagnet (HM/FM) bilayers via the spin Hall effect in the HM and/or the Rashba effect at the interfaces provides an efficient way to switch the magnetization. In the meantime, current induced SOT has also been used to switch the in-plane magnetization in single layers such as ferromagnetic semiconductor (Ga,Mn)As and antiferromagnetic metal CuMnAs with globally or locally broken inversion symmetry. Here we demonstrate the current induced perpendicular magnetization switching in L10 FePt single layer. The current induced spin-orbit effective fields in L10 FePt increase with the chemical ordering parameter (S). In 20 nm FePt films with high S, we observe a large charge-to-spin conversion efficiency and a switching current density as low as 7.0E6 A/cm2. We anticipate our findings may stimulate the exploration of the spin-orbit torques in bulk perpendicular magnetic anisotropic materials and the application of high-efficient perpendicular magnetization switching in single FM layer.

cond-mat.mtrl-sci↗

arXiv2018

Mid-wave and Long-wave IR Linear Dichroism in a Hexagonal Perovskite Chalcogenide

Mid-wave infrared (IR) and long-wave IR spectral ranges are of growing interest in various applications such as thermal imaging, thermography-based remote sensing, and night vision. Materials widely used for IR photodetectors in this regime include cadmium mercury telluride alloys and nanostructures of compound semiconductor. The materials development for IR optics will drive down the cost of IR optical systems and enable larger scale deployment. Here, we report a mid-wave IR responsive material composed of earth abundant and non-toxic elements, Sr1+xTiS3. It has a highly anisotropic quasi-one-dimensional structure similar to hexagonal perovskites. We grew large, high quality single crystals and studied its anisotropic optical properties. We observed two distinct optical absorption edges at ~2.5 um and ~5 um, respectively, for linear polarizations along two principal axes. The material demonstrated strong and broadband linear dichroism spanning mid-wave IR and long-wave IR, with a dichroitic ratio of up to 22.

cond-mat.mtrl-sci↗

arXiv2018

DeePCG: constructing coarse-grained models via deep neural networks

We introduce a general framework for constructing coarse-grained potential models without ad hoc approximations such as limiting the potential to two- and/or three-body contributions. The scheme, called Deep Coarse-Grained Potential (abbreviated DeePCG), exploits a carefully crafted neural network to construct a many-body coarse-grained potential. The network is trained with full atomistic data in a way that preserves the natural symmetries of the system. The resulting model is very accurate and can be used to sample the configurations of the coarse-grained variables in a much faster way than with the original atomistic model. As an application we consider liquid water and use the oxygen coordinates as the coarse-grained variables, starting from a full atomistic simulation of this system at the ab-initio molecular dynamics level. We found that the two-body, three-body and higher order oxygen correlation functions produced by the coarse-grained and full atomistic models agree very well with each other, illustrating the effectiveness of the DeePCG model on a rather challenging task.

physics.chem-ph↗

arXiv2018

Reinforced dynamics for enhanced sampling in large atomic and molecular systems

A new approach for efficiently exploring the configuration space and computing the free energy of large atomic and molecular systems is proposed, motivated by an analogy with reinforcement learning. There are two major components in this new approach. Like metadynamics, it allows for an efficient exploration of the configuration space by adding an adaptively computed biasing potential to the original dynamics. Like deep reinforcement learning, this biasing potential is trained on the fly using deep neural networks, with data collected judiciously from the exploration and an uncertainty indicator from the neural network model playing the role of the reward function. Parameterization using neural networks makes it feasible to handle cases with a large set of collective variables. This has the potential advantage that selecting precisely the right set of collective variables has now become less critical for capturing the structural transformations of the system. The method is illustrated by studying the full-atom, explicit solvent models of alanine dipeptide and tripeptide, as well as the system of a polyalanine-10 molecule with 20 collective variables.

physics.chem-ph↗

arXiv2018

Applicability of Kerker preconditioning scheme to the self-consistent density functional theory calculations of inhomogeneous systems

Kerker preconditioner, based on the dielectric function of homogeneous electron gas, is designed to accelerate the self-consistent field (SCF) iteration in the density functional theory (DFT) calculations. However, question still remains regarding its applicability to the inhomogeneous systems. In this paper, we develop a modified Kerker preconditioning scheme which captures the long-range screening behavior of inhomogeneous systems thus improve the SCF convergence. The effectiveness and efficiency is shown by the tests on long-z slabs of metals, insulators and metal-insulator contacts. For situations without a priori knowledge of the system, we design the a posteriori indicator to monitor if the preconditioner has suppressed charge sloshing during the iterations. Based on the a posteriori indicator, we demonstrate two schemes of the self-adaptive configuration for the SCF iteration.

cond-mat.mtrl-sci↗

arXiv2017

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

Recent developments in many-body potential energy representation via deep learning have brought new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Here we describe DeePMD-kit, a package written in Python/C++ that has been designed to minimize the effort required to build deep learning based representation of potential energy and force field and to perform molecular dynamics. Potential applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems. DeePMD-kit is interfaced with TensorFlow, one of the most popular deep learning frameworks, making the training process highly automatic and efficient. On the other end, DeePMD-kit is interfaced with high-performance classical molecular dynamics and quantum (path-integral) molecular dynamics packages, i.e., LAMMPS and the i-PI, respectively. Thus, upon training, the potential energy and force field models can be used to perform efficient molecular simulations for different purposes. As an example of the many potential applications of the package, we use DeePMD-kit to learn the interatomic potential energy and forces of a water model using data obtained from density functional theory. We demonstrate that the resulted molecular dynamics model reproduces accurately the structural information contained in the original model.

physics.comp-ph↗

arXiv2017

Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics

We introduce a scheme for molecular simulations, the Deep Potential Molecular Dynamics (DeePMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is "first principle-based" in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate protocol in a variety of systems, including bulk materials and molecules. In all these cases, DeePMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.

physics.comp-ph↗

arXiv2017

Privacy Preserving and Collusion Resistant Energy Sharing

Energy has been increasingly generated or collected by different entities on the power grid (e.g., universities, hospitals and householdes) via solar panels, wind turbines or local generators in the past decade. With local energy, such electricity consumers can be considered as "microgrids" which can simulataneously generate and consume energy. Some microgrids may have excessive energy that can be shared to other power consumers on the grid. To this end, all the entities have to share their local private information (e.g., their local demand, local supply and power quality data) to each other or a third-party to find and implement the optimal energy sharing solution. However, such process is constrained by privacy concerns raised by the microgrids. In this paper, we propose a privacy preserving scheme for all the microgrids which can securely implement their energy sharing against both semi-honest and colluding adversaries. The proposed approach includes two secure communication protocols that can ensure quantified privacy leakage and handle collusions.

cs.CR↗

arXiv2017

Atomically-thin Femtojoule Filamentary Memristor

The morphology and dimension of the conductive filament formed in a memristive device are strongly influenced by the thickness of its switching medium layer. Aggressive scaling of this active layer thickness is critical towards reducing the operating current, voltage and energy consumption in filamentary type memristors. Previously, the thickness of this filament layer has been limited to above a few nanometers due to processing constraints, making it challenging to further suppress the on-state current and the switching voltage. Here, we study the formation of conductive filaments in a material medium with sub-nanometer thickness, formed through the oxidation of atomically-thin two-dimensional boron nitride. The resulting memristive device exhibits sub-nanometer filamentary switching with sub-pA operation current and femtojoule per bit energy consumption. Furthermore, by confining the filament to the atomic scale, we observe current switching characteristics that are distinct from that in thicker medium due to the profoundly different atomic kinetics. The filament morphology in such an aggressively scaled memristive device is also theoretically explored. These ultra-low energy devices are promising for realizing femtojoule and sub-femtojoule electronic computation, which can be attractive for applications in a wide range of electronics systems that desire ultra-low power operation.

physics.app-ph↗

arXiv2017

Information diffusion in interconnected heterogeneous networks

In this paper, we are interested in modeling the diffusion of information in a multilayer network using thermodynamic diffusion approach. State of each agent is viewed as a topic mixture represented by a distribution over multiple topics. We have observed and learned diffusion-related thermodynamical patterns in the training data set, and we have used the estimated diffusion structure to predict the future states of the agents. A priori knowledge of a fraction of the state of all agents changes the problem to be a Kalman predictor problem that refines the predicted system state using the error in estimation of the agents. A real world Twitter data set is then used to evaluate and validate our information diffusion model.

cs.SI↗

arXiv2017

The optimal particle-mesh interpolation basis

The fast Ewald methods are widely used to compute the point-charge electrostatic interactions in molecular simulations. The key step that introduces errors in the computation is the particle-mesh interpolation. In this work, the optimal interpolation basis is derived by minimizing the estimated error of the fast Ewald method. The basis can be either general or model specific, depending on whether or not the charge correlation has been taken into account. By using the TIP3P water as an example system, we demonstrate that the general optimal basis is always more accurate than the B-spline basis in the investigated parameter range, while the computational cost is at most 5\% more expensive. In some cases, the optimal basis is found to be two orders of magnitude more accurate. The model specific optimal basis further improves the accuracy of the general optimal basis, but requires more computational effort in the optimization, and may not be transferable to systems with different charge correlations. Therefore, the choice between the general and model specific optimal bases is a trade-off between the generality and the accuracy.

physics.chem-ph↗

arXiv2017

Three-dimensional Pentagon Carbon with a genesis of emergent fermions

Carbon, the basic building block of our universe, enjoys a vast number of allotropic structures. Owing to its bonding characteristic, most carbon allotropes possess the motif of hexagonal rings. Here, with first-principles calculations, we discover a new metastable three-dimensional carbon allotrope entirely composed of pentagon rings. The unique structure of this "Pentagon Carbon" leads to extraordinary electronic properties, making it a cornucopia of emergent topological fermions. Under lattice strain, Pentagon Carbon exhibits topological phase transitions, generating a series of novel quasiparticles, from isospin-1 triplet fermions, to triply-degenerate fermions, and further to concatenated Weyl-loop fermions. Its Landau level spectrum also exhibits distinct features, including a huge number of almost degenerate chiral Landau bands, implying pronounced magneto-transport signals. Our work not only discovers a remarkable carbon allotrope with highly rare structural motifs, it also reveals a fascinating hierarchical particle genesis with novel topological fermions beyond the Dirac and Weyl paradigm.

cond-mat.mtrl-sci↗

arXiv2017

Spatial-Temporal Imaging of Anisotropic Photocarrier Dynamics in Black Phosphorus

As an emerging single elemental layered material with a low symmetry in-plane crystal lattice, black phosphorus (BP) has attracted significant research interest owing to its unique electronic and optoelectronic properties, including its widely tunable bandgap, polarization dependent photoresponse and highly anisotropic in-plane charge transport. Despite extensive study of the steady-state charge transport in BP, there has not been direct characterization and visualization of the hot carriers dynamics in BP immediately after photoexcitation, which is crucial to understanding the performance of BP-based optoelectronic devices. Here we use the newly developed scanning ultrafast electron microscopy (SUEM) to directly visualize the motion of photo-excited hot carriers on the surface of BP in both space and time. We observe highly anisotropic in-plane diffusion of hot holes, with a 15-times higher diffusivity along the armchair (x-) direction than that along the zigzag (y-) direction. Our results provide direct evidence of anisotropic hot carrier transport in BP and demonstrate the capability of SUEM to resolve ultrafast hot carrier dynamics in layered two-dimensional materials.

cond-mat.mtrl-sci↗