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Hongbin Zhang

Publications and source records attributed to Hongbin Zhang.

At least 73 records · Page 4Linked to original sources
arXiv2023

Janus-graphene: a two-dimensional half-auxetic carbon allotropes with non-chemical Janus configuration

The asymmetric properties of Janus two-dimensional materials commonly depend on chemical effects, such as different atoms, elements, material types, etc. Herein, based on carbon gene recombination strategy, we identify an intrinsic non-chemical Janus configuration in a novel purely sp$^2$ hybridized carbon monolayer, named as Janus-graphene. With the carbon gene of tetragonal, hexagonal, and octagonal rings, the spontaneous unilateral growth of carbon atoms drives the non-chemical Janus configuration in Janus-graphene, which is totally different from the chemical effect in common Janus materials such as MoSSe. A structure-independent half-auxetic behavior is mapped in Janus-graphene that the structure maintains expansion whether stretched or compressed, which lies in the key role of $p_z$ orbital. The unprecedented half-auxeticity in Janus-graphene extends intrinsic auxeticity into pure sp$^2$ hybrid carbon configurations. With the unique half-auxeticity emerged in the non-chemical Janus configuration, Janus-graphene enriches the functional carbon family as a promising candidate for micro/nanoelectronic device applications.

cond-mat.mtrl-sci↗
arXiv2023

Bayesian optimization with active learning of Ta-Nb-Hf-Zr-Ti system for spin transport properties

Designing materials with enhanced spin charge conversion, i.e., with high spin Hall conductivity (SHC) and low longitudinal electric conductivity (hence large spin Hall angle (SHA)), is a challenging task, especially in the presence of a vast chemical space for compositionally complex alloys (CCAs). In this work, focusing on the Ta-Nb-Hf-Zr-Ti system, we confirm that CCAs exhibit significant spin Hall conductivities and propose a multi-objective Bayesian optimization approach (MOBO) incorporated with active learning (AL) in order to screen for the optimal compositions with significant SHC and SHA. As a result, within less than 5 iterations we are able to target the TaZr-dominated systems displaying both high magnitudes of SHC (~-2.0 (10$^{-3}$ $Ω$ cm)$^{-1}$) and SHA (~0.03). The SHC is mainly ascribed to the extrinsic skew scattering mechanism. Our work provides an efficient route for identifying new materials with significant SHE, which can be straightforwardly generalized to optimize other properties in a vast chemical space.

cond-mat.mtrl-sci↗
arXiv2023

Improving CTC-AED model with integrated-CTC and auxiliary loss regularization

Connectionist temporal classification (CTC) and attention-based encoder decoder (AED) joint training has been widely applied in automatic speech recognition (ASR). Unlike most hybrid models that separately calculate the CTC and AED losses, our proposed integrated-CTC utilizes the attention mechanism of AED to guide the output of CTC. In this paper, we employ two fusion methods, namely direct addition of logits (DAL) and preserving the maximum probability (PMP). We achieve dimensional consistency by adaptively affine transforming the attention results to match the dimensions of CTC. To accelerate model convergence and improve accuracy, we introduce auxiliary loss regularization for accelerated convergence. Experimental results demonstrate that the DAL method performs better in attention rescoring, while the PMP method excels in CTC prefix beam search and greedy search.

cs.CL↗
arXiv2023

New insights into tackling large amplitude oscillatory shear (LAOS): an analytic LAOS approach (aLAOS)

Large amplitude oscillatory shear (LAOS) has been widely applied for performing rheological analysis of complex fluids, and the Fourier transform (FT) rheology is considered one of the mainstays in LAOS since FT is an essential mathematic tool that is applied in numerous fields of signal analysis. However, the challenge of understanding FT, diverse mathematical frameworks, and complicated data processing render existing LAOS methods hard to be effectively used. As a result, their accessibility and implementation are limited in practice. Thus, the development of novel LAOS methods remains an unmet need. In this context, a newly analytic LAOS (aLAOS) approach was deduced here to tackle LAOS by the reverse use of the methodology of the FT rheology. More specifically, the rheological signals and measures can be precisely reconstructed based on the established equations involving Fourier coefficients instead of processing numerous discrete points of the Lissajous curves. Furthermore, considering the acquired results from the LAOS tests on the various typical yield stress fluids, it was demonstrated that the aLAOS could unify the mainstream LAOS methods into one Fourier coefficient-based framework and visually distinguish the contribution of arbitrary higher harmonics. Consequently, the proposed approach is arising as a promising candidate for the convenient treatment of LAOS, which could also highly promote the multi-field applications of LAOS.

physics.flu-dyn↗
arXiv2023

High-throughput design of all-d-metal Heusler alloys for magnetocaloric applications

Due to their versatile composition and customizable properties, A$_2$BC Heusler alloys have found applications in magnetic refrigeration, magnetic shape memory effects, permanent magnets, and spintronic devices. The discovery of all-$d$-metal Heusler alloys with improved mechanical properties compared to those containing main group elements, presents an opportunity to engineer Heuslers alloys for energy-related applications. Using high-throughput density functional theory calculations, we screened magnetic all-$d$-metal Heusler compounds and identified 686 (meta)stable compounds. Our detailed analysis revealed that the inverse Heusler structure is preferred when the electronegativity difference between the A and B/C atoms is small, contrary to conventional Heusler alloys. Additionally, our calculations of Pugh ratios and Cauchy pressures demonstrated that ductile and metallic bonding are widespread in all-$d$-metal Heuslers, supporting their enhanced mechanical behaviour. We identified 49 compounds with a double-well energy surface based on Bain path calculations and magnetic ground states, indicating their potential as candidates for magnetocaloric and shape memory applications. Furthermore, by calculating the free energies, we propose that 11 compounds exhibit structural phase transitions, and propose isostructural substitution to enhance the magnetocaloric effect.

cond-mat.mtrl-sci↗
arXiv2023

Slidephononics: Tailoring Thermal Transport Properties by van der Waals Sliding

By interlayer sliding in van der Waals (vdW) materials, the switching electric polarization of ultrathin ferroelectric materials leads to the widely studied slidetronics. In this work, we report that such sliding can further tailor anharmonic effects and hence thermal transport properties due to the changed intrinsic coupling between atomic layers. And we propose an unprecedented concept dubbed as slidephononics, where the phonons and associated physical properties can be controlled by varying the intrinsic stacking configurations of slidetronic vdW materials. Based on the state-of-the-art first-principles calculations, it is demonstrated that the thermal conductivity of boron nitride (BN) bilayers can be significantly modulated (by up to four times) along the sliding pathways. Detailed analysis reveals that the variation of thermal conductivities can be attributed to the tunable (de-)coupling of the out-of-plane acoustic phonon branches with the other phonon modes, which is induced by the interlayer charge transfer. Such strongly modulated thermal conductivity via interlayer sliding in vdW materials paves the way to engineer thermal management materials in emerging vdW electronic devices, which would shed light on future studies of slidephononics.

cond-mat.mtrl-sci↗
arXiv2023

Accelerated Screening of Ternary Chalcogenides for High-Performance Optoelectronic Materials

Chalcogenides, which refer to chalcogen anions, have attracted considerable attention in multiple fields of applications, such as optoelectronics, thermoelectrics, transparent contacts, and thin film transistors. In comparison to oxide counterparts, chalcogenides have demonstrated higher mobility and \textit{p}-type dopability, owing to larger orbital overlaps between metal-X covalent chemical bondings and higher-energy valence bands derived by p-orbitals. Despite the potential of chalcogenides, the number of successfully synthesized compounds remains relatively low compared to oxides, suggesting the presence of numerous unexplored chalcogenides with fascinating physical characteristics. In this study, we implemented a systematic high-throughput screening process combined with first-principles calculations on ternary chalcogenides using 34 crystal structure prototypes. We generated a computational material database containing over 400,000 compounds by exploiting the ion-substitution approach at different atomic sites with elements in the periodic table. The thermodynamic stabilities of the candidates were validated using the chalcogenides included in the Open Quantum Materials Database. Moreover, we trained a model based on Crystal Graph Convolutional Neural Networks to predict the thermodynamic stability of novel materials. Furthermore, we theoretically evaluated the electronic structures of the stable candidates using accurate hybrid functionals. A series of in-depth characteristics, including the carrier effective masses, electronic configuration, and photovoltaic conversion efficiency, was also investigated. Our work provides useful guidance for further experimental research in the synthesis and characterization of such chalcogenides as promising candidates, as well as charting the stability and optoelectronic performance of ternary chalcogenides.

cond-mat.mtrl-sci↗
arXiv2023

Magnetic properties of Nd6Fe13Cu single crystals

The understanding of coercivity mechanism in high performance Nd-Fe-B permanent magnets relies on the analysis of the magnetic properties of all phases present in the magnets. By adding Cu in such compounds, a new Nd6Fe13Cu grain boundary phase is formed, however, the magnetic properties of this phase and its role in the magnetic decoupling of the matrix Nd2Fe14B grains are still insufficiently studied. In this work, we have grown Nd6Fe13Cu single crystals by the reactive flux method and studied their magnetic properties in detail. It is observed that below the Néel temperature (TN = 410 K), the Nd6Fe13Cu is antiferromagnetic in zero magnetic field; whereas when a magnetic field is applied along the a-axis, a spin-flop transition occurs at approx. 6 T, indicating a strong competition between antiferromagnetic and ferromagnetic interactions in two Nd layers below and above the Cu layers. Our atomistic spin dynamics simulation confirms that an increase in temperature and/or magnetic field can significantly change the antiferromagnetic coupling between the two Nd layers below and above the Cu layers, which, in turn, is the reason for the observed spin-flop transition. These results suggest that the role of antiferromagnetic Nd6Fe13Cu grain boundary phase in the coercivity enhancement of Nd-Fe-B-Cu magnets is more complex than previously thought, mainly due to the competition between its antiferro- and ferro-magnetic exchange interactions.

cond-mat.mtrl-sci↗
arXiv2023

Designing magnetocaloric materials for hydrogen liquefaction with light rare-earth Laves phases

Magnetocaloric hydrogen liquefaction could be a "game-changer" for liquid hydrogen industry. Although heavy rare-earth-based magnetocaloric materials show strong magnetocaloric effects in the temperature range required by hydrogen liquefaction (77 ~ 20 K), the high resource criticality of the heavy rare-earth elements is a major obstacle for upscaling this emerging liquefaction technology. In contrast, the higher abundances of the light rare-earth elements make their alloys highly appealing for magnetocaloric hydrogen liquefaction. Via a mean-field approach, it is demonstrated that tuning the Curie temperature ($T_C$) of an idealized light rare-earth-based magnetocaloric material towards lower cryogenic temperatures leads to larger maximum magnetic and adiabatic temperature changes ($ΔS_T$ and $ΔT_{ad}$). Especially in the vicinity of the condensation point of hydrogen (20 K), $ΔS_T$ and $ΔT_{ad}$ of the optimized light rare-earth-based material are predicted to show significantly large values. Following the mean-field approach and taking the chemical and physical similarities of the light rare-earth elements into consideration, a method of designing light rare-earth intermetallic compounds for hydrogen liquefaction is proposed: tunning $T_C$ of a rare-earth alloy to approach 20 K by mixing light rare-earth elements with different de Gennes factors. By mixing Nd and Pr in Laves phase $(Nd,Pr)Al_2$, and Pr and Ce in Laves phase $(Pr,Ce)Al_2$, a fully light rare-earth intermetallic series with large magnetocaloric effects covering the temperature range required by hydrogen liquefaction is developed, demonstrating a competitive maximum effect compared to the heavy rare-earth compound $DyAl_2$.

cond-mat.mtrl-sci↗
arXiv2023

Element-Specific Study of Magnetic Anisotropy and Hardening in SmCo$_{5-x}$Cu$_{x}$ Thin Films

This work investigates the effect of copper substitution on the magnetic properties of SmCo$_{5}$ thin films synthesized by molecular beam epitaxy. A series of thin films with varying concentrations of Cu were grown under otherwise identical conditions to disentangle structural and compositional effects on the magnetic behavior. The combined experimental and theoretical studies show that Cu substitution at the Co$_{3g}$ sites not only stabilizes the formation of the SmCo$_{5}$ structure but enhances magnetic anisotropy and coercivity. Density functional theory calculations indicate that Sm(Co$_4$Cu$_{3g}$)$_5$ possesses a higher single-ion anisotropy as compared to pure SmCo$_{5}$. In addition, X-ray magnetic circular dichroism reveals that Cu substitution causes an increasing decoupling of the Sm 4\textit{f} and Co 3\textit{d} moments. Scanning transmission electron microscopy confirms predominantly SmCo$_{5}$ phase formation and reveals nanoscale inhomogeneities in the Cu and Co distribution. Our study based on thin film model systems and advanced characterization as well as modeling reveals novel aspects of the complex interplay of intrinsic and extrinsic contributions to magnetic hysteresis in rare earth-based magnets, \textit{i.e.} the combination of increased intrinsic anisotropy due to Cu substitution and the extrinsic effect of inhomogeneous elemental distribution of Cu and Co.

cond-mat.mtrl-sci↗
arXiv2023

A surrogate model for data-driven magnetic stray field calculations

In this contribution we propose a data-driven surrogate model for the prediction of magnetic stray fields in two-dimensional random micro-heterogeneous materials. Since data driven models require thousands of training data sets, FEM simulations appear to be too time consuming. Hence, a stochastic model based on Brownian motion, which utilizes an efficient evaluation of stochastic transition matrices, is applied for the training data generation. For the encoding of the microstructure and the optimization of the surrogate model, two architectures are compared, i.e. the so-called UResNet model and the Fourier Convolutional neural network (FCNN). Here we analyze two FCNNs, one based on the discrete cosine transformation and one based on the complex-valued discrete Fourier transformation. Finally, we compare the magnetic stray fields for independent microstructures (not used in the training set) with results from the FE$^2$ method, a numerical homogenization scheme, to demonstrate the efficiency of the proposed surrogate model.

math.NA↗
arXiv2023

A BERT-based Unsupervised Grammatical Error Correction Framework

Grammatical error correction (GEC) is a challenging task of natural language processing techniques. While more attempts are being made in this approach for universal languages like English or Chinese, relatively little work has been done for low-resource languages for the lack of large annotated corpora. In low-resource languages, the current unsupervised GEC based on language model scoring performs well. However, the pre-trained language model is still to be explored in this context. This study proposes a BERT-based unsupervised GEC framework, where GEC is viewed as multi-class classification task. The framework contains three modules: data flow construction module, sentence perplexity scoring module, and error detecting and correcting module. We propose a novel scoring method for pseudo-perplexity to evaluate a sentence's probable correctness and construct a Tagalog corpus for Tagalog GEC research. It obtains competitive performance on the Tagalog corpus we construct and open-source Indonesian corpus and it demonstrates that our framework is complementary to baseline method for low-resource GEC task.

cs.CL↗
arXiv2023

An optical analogue for rotating BTZ black holes

We demonstrate an optical realization for the rotating BTZ black hole using the recent popular photon fluid model in an optical vortex but with a new proposed expression for the optical phase. We also give the numerical realization for the optical vortex to ensure that it can be generated experimentally. Different from the earlier suggestions for the analogue rotating black holes, our proposal includes an inner horizon in the analogue black hole structure. Such structure can keep for a long distance for the convenience of observing analogue Hawking or Penrose radiations.

gr-qc↗
arXiv2023

Shielding of Penrose superradiance in optical black holes

We investigate the effect of superradiance shielding for the analogue rotating black holes simulated by optical vortices by calculating the radial motion of massless particles in such spacetime background. We add the conditions $E 0$ to judge the classically forbidden region of superradiance. It is found that the superradiance forbidden region exists near the static limit inside the ergosphere,which will limit the classical Penrose process for the particles with some specific energies and angular momenta. Once these particles satisfying the superradiance conditions aremeasured at the outside of the ergosphere, this shows that the Penrose process can be quantum.

gr-qc↗
arXiv2023

Giant anomalous Hall and Nernst conductivities in magnetic all-$d$ metal Heusler alloys

All-$d$ Heuslers are a category of novel compounds combining versatile functionalities such as caloric responses and spintronics with enhanced mechanical properties. Despite the promising transport properties (anomalous Hall (AHC) and anomalous Nernst (ANC) conductivities) shown in the conventional Co$_2$XY Heuslers with $p$-$d$ hybridization, the all-$d$ Heuslers with only $d$-$d$ hybridization open a new horizon to search for new candidates with outstanding transport properties. In this work, we evaluate the AHC and ANC for thermodynamically stable ferro/ferri-magnetic all-$d$-metal regular Heusler compounds based on high-throughput first-principles calculations. It is observed that quite a few materials exhibit giant AHCs and ANCs, such as cubic Re$_2$TaMn with an AHC of 2011 S/cm, and tetragonal Pt$_2$CrRh with an AHC of 1966 S/cm and an ANC of 7.50 A/mK. Comprehensive analysis on the electronic structure reveals that the high AHC can be attributed to the occurrence of the Weyl nodes or gapped nodal lines in the neighbourhood of the Fermi level. The correlations between such transport properties and the number of valence electrons are also thoroughly investigated, which provides a practical guidance to tailor AHC and ANC via chemical doping for transverse thermoelectric applications.

cond-mat.mtrl-sci↗
arXiv2023

Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures

Autonomous materials discovery with desired properties is one of the ultimate goals for materials science, and the current studies have been focusing mostly on high-throughput screening based on density functional theory calculations and forward modelling of physical properties using machine learning. Applying the deep learning techniques, we have developed a generative model which can predict distinct stable crystal structures by optimizing the formation energy in the latent space. It is demonstrated that the optimization of physical properties can be integrated into the generative model as on-top screening or backwards propagator, both with their own advantages. Applying the generative models on the binary Bi-Se system reveals that distinct crystal structures can be obtained covering the whole composition range, and the phases on the convex hull can be reproduced after the generated structures are fully relaxed to the equilibrium. The method can be extended to multicomponent systems for multi-objective optimization, which paves the way to achieve the inverse design of materials with optimal properties.

physics.comp-ph↗
arXiv2023

Autonomous atomic Hamiltonian construction and active sampling of x-ray absorption spectroscopy by adversarial Bayesian optimization

X-ray absorption spectroscopy (XAS) is a well-established method for in-depth characterization of the electronic structure due to its sensitivity to the local coordination and electronic states of the active ions. In practice hundreds of energy points should be sampled during the XAS measurement, most of which are redundant and do not contain important information. In addition, it is also a tedious procedure to estimate reasonable parameters in the atomic Hamiltonian for mechanistic understanding. We implemented an Adversarial Bayesian optimization (ABO) algorithm comprising two coupled BOs to automatically fit the multiplet model Hamiltonian and meanwhile to sample effectively based on active learning. Taking NiO as an example, for simulated spectra which can be well fitted by the atomic model, we found that less than 30 sampling points are enough to obtain the complete XAS with the corresponding crystal field or charge transfer model, which can be selected based on intuitive hypothesis learning. Further application on the experimental spectra, it revealed that less than 80 sampling points can already give reasonable XAS and reliable atomic model parameters. Our ABO algorithm has a great potential for future application in automated physics-driven XAS analysis and active learning sampling.

cond-mat.mtrl-sci↗
arXiv2022

Systems-theoretic Hazard Analysis of Digital Human-System Interface Relevant to Reactor Trip

Human-system interface is one of the key advanced design features applied to modern digital instrumentation and control systems of nuclear power plants. The conventional design is based on a compact workstation-based system within the control room. The compact workstation provides both a strategic operating environment while also a convenient display for plant status information necessary to the operator. The control environment is further enhanced through display panels, visual and auditory alarms, and procedure systems. However, just like the legacy control, the HSI should incorporate diversity to demonstrate sufficient defense-in-depth protection against common cause failures of the safety system. Furthermore, the vulnerability of the HSI is affected by a plethora of factors, such as human error, cyberattacks, software common cause failures, etc., that complicate the design and analysis. Therefore, this work aims to identify and evaluate existing system vulnerabilities to support the licensing, deployment and operation of HSI designs, especially the functions that are relevant to a reactor trip. We performed a systematic hazard analysis to investigate potential vulnerabilities within the HSI design using the novel redundancy-guided systems-theoretic hazard analysis. This method was developed and demonstrated by Idaho National Laboratory under a project initiated by the Risk-Informed Systems Analysis Pathway of the U.S. Department of Energy's Light Water Reactor Sustainability Program. The goal of the project is to develop a strong technical basis for risk assessment strategies to support effective, reliable, and licensable digital instrumentation and control technologies.

cs.HC↗