arXiv subjects

Hongbin Zhang

Publications and source records attributed to Hongbin Zhang.

At least 109 records · Page 6Linked to original sources

arXiv2021

Enhanced anomalous Nernst effects in ferromagnetic materials driven by Weyl nodes

Based on high-throughput first-principles calculations, we evaluated the anomalous Hall and anomalous Nernst conductivities of 266 transition-metal-based ferromagnetic compounds. Detailed analysis based on the symmetries and Berry curvatures reveals that the origin of singular-like behaviour of anomalous Hall/Nernst conductivities can be mostly attributed to the appearance of Weyl nodes or nodal lines located in the proximity of the Fermi energy, which can be further tailored by external stimuli such as biaxial strains and magnetic fields. Moreover, such calculations are enabled by the automated construction of Wannier functions with a success rate of 92%, which paves the way to perform accurate high-throughput evaluation of the physical properties such as the transport properties using the Wannier interpolation

cond-mat.mtrl-sci↗

arXiv2021

Inverse design of crystal structures for multicomponent systems

We developed an inverse design framework enabling automated generation of stable multi-component crystal structures by optimizing the formation energies in the latent space based on reversible crystal graphs with continuous representation. It is demonstrated that 9,160 crystal structures can be generated out of 50,000 crystal graphs, leading to 8,310 distinct cases using a training set of 52,615 crystal structures from Materials Project. Detailed analysis on 15 selected systems reveals that unreported crystal structures below the convex hull can be discovered in 6 material systems. Moreover, the generation efficiency can be further improved by considering extra hypothetical structures in the training. This paves the way to perform inverse design of multicomponent materials with possible multi-objective optimization.

cond-mat.mtrl-sci↗

arXiv2020

Thermal conductivity of h-BN monolayers using machine learning interatomic potential

Thermal management materials are of critical importance for engineering miniaturized electronic devices, where theoretical design of such materials demands the evaluation of thermal conductivities which are numerically expensive. In this work, we applied the recently developed machine learning interatomic potential (MLIP) to evaluate the thermal conductivity of hexagonal boron nitride monolayers. The MLIP is obtained using the Gaussian approximation potential (GAP) method, and the resulting lattice dynamical properties and thermal conductivity are compared with those obtained from explicit frozen phonon calculations. It is observed that accurate thermal conductivity can be obtained based on MLIP constructed with about 30% representative configurations, and the high-order force constants provide a more reliable benchmark on the quality of MLIP than the harmonic approximation.

cond-mat.mtrl-sci↗

arXiv2020

Tailoring magnetocaloric effect in all-d-metal Ni-Co-Mn-Ti Heusler alloys: a combined experimental and theoretical study

Novel Ni-Co-Mn-Ti all-d-metal Heusler alloys are exciting due to large multicaloric effects combined with enhanced mechanical properties. An optimized heat treatment for a series of these compounds leads to very sharp phase transitions in bulk alloys with isothermal entropy changes of up to 38 J kg$^{-1}$ K$^{-1}$ for a magnetic field change of 2 T. The differences of as-cast and annealed samples are analyzed by investigating microstructure and phase transitions in detail by optical microscopy. We identify different grain structures as well as stoichiometric (in)homogenieties as reasons for differently sharp martensitic transitions after ideal and non-ideal annealing. We develop alloy design rules for tuning the magnetostructural phase transition and evaluate specifically the sensitivity of the transition temperature towards the externally applied magnetic fields ($\frac{dT_t}{μ_0dH}$) by analyzing the different stoichiometries. We then set up a phase diagram including martensitic transition temperatures and austenite Curie temperatures depending on the e/a ratio for varying Co and Ti content. The evolution of the Curie temperature with changing stoichiometry is compared to other Heusler systems. Density Functional Theory calculations reveal a correlation of T$_C$ with the stoichiometry as well as with the order state of the austenite. This combined approach of experiment and theory allows for an efficient development of new systems towards promising magnetocaloric properties. Direct adiabatic temperature change measurements show here the largest change of -4 K in a magnetic field change of 1.93 T for Ni$_{35}$Co$_{15}$Mn$_{37}$Ti$_{13}$.

cond-mat.mtrl-sci↗

arXiv2020

Designing of Magnetic MAB Phases for Energy Applications

Based on high-throughput density functional theory calculations, we performed screening for stable magnetic MAB compounds and predicted potential strong magnets for permanent magnet and magnetocaloric applications. The thermodynamical, mechanical, and dynamical stabilities are systematically evaluated, resulting in 21 unreported compounds on the convex hull, and 434 materials being metastable considering convex hull tolerance to be 100 meV/atom. Analysis based on the Hume-Rothery rules revealed that the valence electron concentration and size factor difference are of significant importance in determining the stability, with good correspondence with the local atomic bonding. We found 71 compounds with the absolute value of magneto-crystalline anisotropy energy above 1.0 MJ/m$^3$ and 23 compounds with a uniaxial anisotropy greater than 0.4 MJ/m$^3$, which are potential gap magnets. Based on the magnetic deformation proxy, 99 compounds were identified as potential materials with interesting magnetocaloric performance.

cond-mat.mtrl-sci↗

arXiv2020

Multifunctional Antiperovskites driven by Strong Magnetostructural Coupling

Based on density functional theory calculations, we elucidated the origin of multifunctional properties for cubic antiperovskites with noncollinear magnetic ground states, which can be attributed to strong isotropic and anisotropic magnetostructural coupling. 16 out of 54 stable magnetic antiperovskites M$_3$XZ (M = Cr, Mn, Fe, Co, and Ni; X = selected elements from Li to Bi except for noble gases and 4f rare-earth metals; and Z = C and N) are found to exhibit the $Γ_{4g}$/$Γ_{5g}$ (i.e., characterized by irreducible representations) antiferromagnetic magnetic configurations driven by frustrated exchange coupling and strong magnetocrystalline anisotropy. Using the magnetic deformation as an effective proxy, the isotropic magnetostructural coupling is characterized, and it is observed that the paramagnetic state is critical to understand the experimentally observed negative thermal expansion and to predict the magnetocaloric performance. Moreover, the piezomagnetic and piezospintronic effects induced by biaxial strain are investigated. It is revealed that there is not a strong correlation between the induced magnetization and anomalous Hall conductivities by the imposed strain. Interestingly, the anomalous Hall/Nernst conductivities can be significantly tailored by the applied strain due to the fine-tuning of the Weyl points energies, leading to promising spintronic applications.

cond-mat.mtrl-sci↗

arXiv2020

High-throughput Design of Magnetic Materials

Materials design based on density functional theory (DFT) calculations is an emergent field of great potential to accelerate the development and employment of novel materials. Magnetic materials play an essential role in green energy applications as they provide efficient ways of harvesting, converting, and utilizing energy. In this review, after a brief introduction to the major functionalities of magnetic materials, we demonstrated the fundamental properties which can be tackled via high-throughput DFT calculations, with a particular focus on the current challenges and feasible solutions. Successful case studies are summarized on several classes of magnetic materials, followed by bird-view perspectives for the future.

cond-mat.mtrl-sci↗

arXiv2020

Deep Learning Interfacial Momentum Closures in Coarse-Mesh CFD Two-Phase Flow Simulation Using Validation Data

Multiphase flow phenomena have been widely observed in the industrial applications, yet it remains a challenging unsolved problem. Three-dimensional computational fluid dynamics (CFD) approaches resolve of the flow fields on finer spatial and temporal scales, which can complement dedicated experimental study. However, closures must be introduced to reflect the underlying physics in multiphase flow. Among them, the interfacial forces, including drag, lift, turbulent-dispersion and wall-lubrication forces, play an important role in bubble distribution and migration in liquid-vapor two-phase flows. Development of those closures traditionally rely on the experimental data and analytical derivation with simplified assumptions that usually cannot deliver a universal solution across a wide range of flow conditions. In this paper, a data-driven approach, named as feature-similarity measurement (FSM), is developed and applied to improve the simulation capability of two-phase flow with coarse-mesh CFD approach. Interfacial momentum transfer in adiabatic bubbly flow serves as the focus of the present study. Both a mature and a simplified set of interfacial closures are taken as the low-fidelity data. Validation data (including relevant experimental data and validated fine-mesh CFD simulations results) are adopted as high-fidelity data. Qualitative and quantitative analysis are performed in this paper. These reveal that FSM can substantially improve the prediction of the coarse-mesh CFD model, regardless of the choice of interfacial closures, and it provides scalability and consistency across discontinuous flow regimes. It demonstrates that data-driven methods can aid the multiphase flow modeling by exploring the connections between local physical features and simulation errors.

physics.flu-dyn↗

arXiv2020

A Redundancy-Guided Approach for the Hazard Analysis of Digital Instrumentation and Control Systems in Advanced Nuclear Power Plants

Digital instrumentation and control (I&C) upgrades are a vital research area for nuclear industry. Despite their performance benefits, deployment of digital I&C in nuclear power plants (NPPs) has been limited. Digital I&C systems exhibit complex failure modes including common cause failures (CCFs) which can be difficult to identify. This paper describes the development of a redundancy-guided application of the Systems-Theoretic Process Analysis (STPA) and Fault Tree Analysis (FTA) for the hazard analysis of digital I&C in advanced NPPs. The resulting Redundancy-guided System-theoretic Hazard Analysis (RESHA) is applied for the case study of a representative state-of-the-art digital reactor trip system. The analysis qualitatively and systematically identifies the most critical CCFs and other hazards of digital I&C systems. Ultimately, RESHA can help researchers make informed decisions for how, and to what degree, defensive measures such as redundancy, diversity, and defense-in-depth can be used to mitigate or eliminate the potential hazards of digital I&C systems.

eess.SY↗

arXiv2020

Using Deep Learning to Explore Local Physical Similarity for Global-scale Bridging in Thermal-hydraulic Simulation

Current system thermal-hydraulic codes have limited credibility in simulating real plant conditions, especially when the geometry and boundary conditions are extrapolated beyond the range of test facilities. This paper proposes a data-driven approach, Feature Similarity Measurement FFSM), to establish a technical basis to overcome these difficulties by exploring local patterns using machine learning. The underlying local patterns in multiscale data are represented by a set of physical features that embody the information from a physical system of interest, empirical correlations, and the effect of mesh size. After performing a limited number of high-fidelity numerical simulations and a sufficient amount of fast-running coarse-mesh simulations, an error database is built, and deep learning is applied to construct and explore the relationship between the local physical features and simulation errors. Case studies based on mixed convection have been designed for demonstrating the capability of data-driven models in bridging global scale gaps.

cs.LG↗

arXiv2020

Effect of N, C and B interstitials on the structural and magnetic properties of alloys with Cu$_3$Au-structure

High-throughput density functional calculations are used to investigate the effect of interstitial B, C and N atoms on 21 alloys reported to crystallize in the cubic Cu$_3$Au structure. It is shown that the interstitials can have a significant impact on the magneto-crystalline anisotropy energy (MAE), the thermodynamic stability and the magnetic ground state structure, making these alloys interesting for hard magnetic, magnetocaloric and other applications. For 29 alloy/interstitial combinations the formation of stable alloys with interstitial concentrations above 5\% is expected. In Ni$_3$Mn interstitial N induces a tetragonal distortion with substantial uniaxial MAE for realistic N concentrations. Mn$_3X$N$_x$ ($X$=Rh, Ir, Pt and Sb) are identified as alloys with strong magneto-crystalline anisotropy. For Mn$_3$Ir we find a strong enhancement of the MAE upon N alloying in the most stable collinear ferrimagnetic state as well as in the non-collinear magnetic ground state. Mn$_3$Ir and Mn$_3$IrN show also interesting topological transport properties. The effect of N concentration and strain on the magnetic properties are discussed. Further, the huge impact of N on the MAE of Mn$_3$Ir and a possible impact of interstitial N on amorphous Mn$_3$Ir, a material that is indispensable in today's data storage devices, are discussed at hand of the electronic structure. For Mn$_3$Sb, non-collinear, ferrimagnetic and ferromagnetic states are very close in energy, making this material potentially interesting for magnetocaloric applications. For the investigated Mn alloys and competing phases, the determination of the magnetic ground state is essential for a reliable prediction of the phase stability.

cond-mat.mtrl-sci↗

arXiv2019

Tunning Spin Hall conductivities in GeTe by Ferroelectric Polarization

Controlling charge-spin current conversion by electric fields is crucial in spintronic devices, which can be realized in diatom ferroelectric semiconductor GeTe where it is established that ferroelectricity can change the spin texture. We demonstrated that the spin Hall conductivity (SHC) can be further tuned by ferroelectricity based on the density functional theory calculations. The spin texture variation driven by the electric fields was elucidated from the symmetry point of view, highlighting the interlocked spin and orbital degrees of freedom. We observed that the origin of SHC can be attributed to the Rashba effect and the intrinsic spin-orbit coupling. The magnitude of one component of SHC σ_xy^z can reach as large as 100 {\hbar}/e/(Ωcm) in the vicinity of the band edge, which is promising for engineering spintronic devices. Our work on tunable spin transport properties via the ferroelectric polarization brings novel assets into the field of spintronics.

cond-mat.mtrl-sci↗

arXiv2019

Computationally Efficient CFD Prediction of Bubbly Flow using Physics-Guided Deep Learning

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven approach. Instrumental to this framework, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. By defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.

physics.comp-ph↗

arXiv2019

Designing rare-earth free permanent magnets in Heusler alloys via interstitial doping

Based on high-throughput density functional theory calculations, we investigated the effects of light interstitial H, B, C, and N atoms on the magnetic properties of cubic Heusler alloys, with the aim to design new rare-earth free permanent magnets. It is observed that the interstitial atoms induce significant tetragonal distortions, leading to 32 candidates with large ($>$ 0.4 MJ/m$^3$) uniaxial magneto-crystalline anisotropy energies (MAEs) and 10 cases with large in-plane MAEs. Detailed analysis following the the perturbation theory and chemical bonding reveals the strong MAE originates from the local crystalline distortions and thus the changes of the chemical bonding around the interstitials. This provides a valuable way to tailor the MAEs to obtain competitive permanent magnets, filling the gap between high performance Sm-Co/Nd-Fe-B and widely used ferrite/AlNiCo materials.

cond-mat.mtrl-sci↗

arXiv2019

Direct observations of chiral spin textures in van der Waals magnet Fe3GeTe2 nanolayers

In two-dimensional van der Waals (vdW) magnets, the presence of magnetic orders, strong spin-orbit coupling and asymmetry at interfaces is the key ingredient for hosting chiral spin textures. However, experimental evidences for chiral magnetism in vdW magnets remain elusive. Here we demonstrate unambiguously the formation of chiral spin textures in thin Fe3GeTe2 nanoflakes using advanced magnetic electron microscopy and first-principles calculations. Specifically, electron holography analyses reveal the spin configurations of Néel-type, zero-field-stabilized skyrmions in 20-nm-thick Fe3GeTe2 nanoflakes at cryogenic temperature. In situ Lorentz transmission electron microscopy measurements further provide detailed magnetic phase diagrams of chiral spin textures including spirals and skyrmions in Fe3GeTe2 as a function of temperature, applied magnetic field and specimen thickness. First-principles calculations unveil a finite interfacial Dzyaloshinskii-Moriya interaction in the Te/Fe3Ge/Te slabs that induces the spin chirality in Fe3GeTe2. Our discovery of spin chirality in the prototypical vdW Fe3GeTe2 opens up new opportunities for studying chiral magnetism in two-dimensional vdW magnets from both fundamental and applied perspectives.

cond-mat.mtrl-sci↗

arXiv2019

Mixed topological semimetals driven by orbital complexity in two-dimensional ferromagnets

The concepts of Weyl fermions and topological semimetals emerging in three-dimensional momentum space are extensively explored owing to the vast variety of exotic properties that they give rise to. On the other hand, very little is known about semimetallic states emerging in two-dimensional magnetic materials, which present the foundation for both present and future information technology. Here, we demonstrate that including the magnetization direction into the topological analysis allows for a natural classification of topological semimetallic states that manifest in two-dimensional ferromagnets as a result of the interplay between spin-orbit and exchange interactions. We explore the emergence and stability of such mixed topological semimetals in realistic materials, and point out the perspectives of mixed topological states for current-induced orbital magnetism and current-induced domain wall motion. Our findings pave the way to understanding, engineering and utilizing topological semimetallic states in two-dimensional spin-orbit ferromagnets.

cond-mat.mes-hall↗

arXiv2019

Piezospintronic effect in antiperovskite Mn$_3$GaN

Based on first-principles calculations, we investigated the topological transport properties of Mn$_3$GaN with coplanar noncollinear magnetic structures. The intrinsic anomalous Hall conductivity (IAHC) displays a significant dependence with respect to the in-plane magnetization direction between the $Γ_{5g}$ and $Γ_{4g}$ magnetic configurations, where large anomalous Nernst effect (ANE) can be induced by tailoring the magnetization direction. Moreover, we observed strong piezospintronic effect in Mn$_3$GaN, where large IAHC can be induced by moderate epitaxial strain. Symmetry analysis reveals that for both cases, the nonzero IAHC is originated from the spin-orbit coupling instead of the noncollinear magnetic configurations

cond-mat.mtrl-sci↗

arXiv2019

Tuning the metal-insulator transition in epitaxial SrVO3 films by uniaxial strain

Understanding of the metal-insulator transition (MIT) in correlated transition-metal oxides is a fascinating topic in condensed matter physics and a precise control of such transitions plays a key role in developing novel electronic devices. Here we report an effective tuning of the MIT in epitaxial SrVO3 (SVO) films by expanding the out-of-plane lattice constant without changing in-plane lattice parameters, through helium ion irradiation. Upon increase of the ion fluence, we observe a MIT with a crossover from metallic to insulating state in SVO films. A combination of transport and magnetoresistance measurements in SVO at low temperatures reveals that the observed MIT is mainly ascribed to electron-electron interactions rather than disorder-induced localization. Moreover, these results are well supported by the combination of density functional theory and dynamical mean field theory (DFT+DMFT) calculations, further confirming the decrease of the bandwidth and the enhanced electron-electron interactions resulting from the expansion of out-of-plane lattice constant. These findings provide new insights into the understanding of MIT in correlated oxides and perspectives for the design of unexpected functional devices based on strongly correlated electrons.

cond-mat.mtrl-sci↗