arXiv subjects

Qiang Zheng

Publications and source records attributed to Qiang Zheng.

At least 37 records · Page 2Linked to original sources

arXiv2022

Gapless spin-excitations in the superconducting state of a quasi-one-dimensional spin-triplet superconductor

Majorana zero modes form as intrinsic defects in an odd-orbital one-dimensional superconductor thus motivating the search for such materials in the pursuit of Majorana physics. Here, we present combined experimental results and first principles calculations which suggest that quasi-one-dimensional K$_2$Cr$_3$As$_3$ may be such a superconductor. Using inelastic neutron scattering we probe the dynamic spin-susceptibilities of K$_2$Cr$_3$As$_3$ and K$_2$Mo$_3$As$_3$ and show the presence of antiferromagnetic spin-fluctuations in both compounds. Below the superconducting transition, these fluctuations gap in K$_2$Mo$_3$As$_3$ but not in K$_2$Cr$_3$As$_3$. Using first principles calculations, we show that these fluctuations likely arise from nesting on one dimensional features of the Fermi surface. Considering these results we propose that while K$_2$Mo$_3$As$_3$ is a conventional superconductor, K$_2$Cr$_3$As$_3$ is likely a spin-triplet, and consequently, topological superconductor.

cond-mat.supr-con↗

arXiv2022

Uncertainty quantification of two-phase flow in porous media via coupled-TgNN surrogate model

Uncertainty quantification (UQ) of subsurface two-phase flow usually requires numerous executions of forward simulations under varying conditions. In this work, a novel coupled theory-guided neural network (TgNN) based surrogate model is built to facilitate computation efficiency under the premise of satisfactory accuracy. The core notion of this proposed method is to bridge two separate blocks on top of an overall network. They underlie the TgNN model in a coupled form, which reflects the coupling nature of pressure and water saturation in the two-phase flow equation. The TgNN model not only relies on labeled data, but also incorporates underlying scientific theory and experiential rules (e.g., governing equations, stochastic parameter fields, boundary and initial conditions, well conditions, and expert knowledge) as additional components into the loss function. The performance of the TgNN-based surrogate model for two-phase flow problems is tested by different numbers of labeled data and collocation points, as well as the existence of data noise. The proposed TgNN-based surrogate model offers an effective way to solve the coupled nonlinear two-phase flow problem and demonstrates good accuracy and strong robustness when compared with the purely data-driven surrogate model. By combining the accurate TgNN-based surrogate model with the Monte Carlo method, UQ tasks can be performed at a minimum cost to evaluate statistical quantities. Since the heterogeneity of the random fields strongly impacts the results of the surrogate model, corresponding variance and correlation length are added to the input of the neural network to maintain its predictive capacity. The results show that the TgNN-based surrogate model achieves satisfactory accuracy, stability, and efficiency in UQ problems of subsurface two-phase flow.

physics.flu-dyn↗

arXiv2022

Inferring electrochemical performance and parameters of Li-ion batteries based on deep operator networks

The Li-ion battery is a complex physicochemical system that generally takes applied current as input and terminal voltage as output. The mappings from current to voltage can be described by several kinds of models, such as accurate but inefficient physics-based models, and efficient but sometimes inaccurate equivalent circuit and black-box models. To realize accuracy and efficiency simultaneously in battery modeling, we propose to build a data-driven surrogate for a battery system while incorporating the underlying physics as constraints. In this work, we innovatively treat the functional mapping from current curve to terminal voltage as a composite of operators, which is approximated by the powerful deep operator network (DeepONet). Its learning capability is firstly verified through a predictive test for Li-ion concentration at two electrodes. In this experiment, the physics-informed DeepONet is found to be more robust than the purely data-driven DeepONet, especially in temporal extrapolation scenarios. A composite surrogate is then constructed for mapping current curve and solid diffusivity to terminal voltage with three operator networks, in which two parallel physics-informed DeepONets are firstly used to predict Li-ion concentration at two electrodes, and then based on their surface values, a DeepONet is built to give terminal voltage predictions. Since the surrogate is differentiable anywhere, it is endowed with the ability to learn from data directly, which was validated by using terminal voltage measurements to estimate input parameters. The proposed surrogate built upon operator networks possesses great potential to be applied in on-board scenarios, such as battery management system, since it integrates efficiency and accuracy by incorporating underlying physics, and also leaves an interface for model refinement through a totally differentiable model structure.

physics.comp-ph↗

arXiv2021

Structural Origin of Boson Peak in Glasses

Boson peak, the excess low energy excitations in the terahertz regime, is one of the most unique features of disordered systems and has been linked to many anomalous properties of glass materials. The nature and structural origin of the boson peak remain elusive and have been debated for more than a half century mainly due to the lack of real-time and real-space experimental insights of the dynamic phenomenon. In this work we employed femtosecond MeV ultrafast electron diffraction to characterize the atomic dynamics of metallic glasses in real time. The experiment reveals collective atomic oscillations, presented in elastic electron scattering and atomic pair distribution functions, within the boson peak frequency range of 1.0-1.8 THz in both reciprocal and real space. It was found that the oscillation frequency has reciprocal dependence on interatomic pair distances and the corresponding wave velocity experimentally affirms the transverse acoustic wave nature of the boson peak. The observed strong correlation between THz acoustic vibrations and coherent electron scattering provides compelling evidence that the boson peak originates from the collective transverse vibrational modes of structurally ordered atoms in the disordered system.

cond-mat.dis-nn↗

arXiv2021

The microstructural dependence of ionic transport in bi-continuous nanoporous metal

Ionic transports in nanopores hold the key to unlocking the full potential of bi-continuous nanoporous (NP) metals as advanced electrodes in electrochemical devices. The precise control of the uniform NP metal structures also provides us a unique opportunity to understand how complex structures determine transports at nanoscales. For NP Au from the dealloying of a Ag-Au alloy, we can tune the pore size in the range of 13 nm to 2.4 microns and the porosity between 38% and 69% via isothermal coarsening. For NP Ag from the reduction-induced decomposition of AgCl, we can control additionally its structural hierarchy and pore orientation. We measure the effective ionic conductivities of 1 M NaClO4 through these NP metals as membranes, which range from 7% to 44% of that of a free solution, corresponding to calculated pore tortuosities between 2.7 and 1.3. The tortuosity of NP Au displays weak dependences on both the pore size and the porosity, consistent with the observed self-similarity in the coarsening, except for those of pores < 25 nm, which we consider deviating from the well-coarsened pore geometry. For NP Ag, the low tortuosity of the hierarchical structure can be explained with the Maxwell-Garnett equation and that of the oriented structure underlines the random orientation as the cause of slow transport in other NP metals. At last, we achieve high current densities of CO2 reduction with these two low-tortuosity NP Ags, demonstrating the significance of the structure-transport relationships for designing functional NP metals.

physics.app-ph↗

arXiv2021

RockGPT: Reconstructing three-dimensional digital rocks from single two-dimensional slice from the perspective of video generation

Random reconstruction of three-dimensional (3D) digital rocks from two-dimensional (2D) slices is crucial for elucidating the microstructure of rocks and its effects on pore-scale flow in terms of numerical modeling, since massive samples are usually required to handle intrinsic uncertainties. Despite remarkable advances achieved by traditional process-based methods, statistical approaches and recently famous deep learning-based models, few works have focused on producing several kinds of rocks with one trained model and allowing the reconstructed samples to satisfy certain given properties, such as porosity. To fill this gap, we propose a new framework, named RockGPT, which is composed of VQ-VAE and conditional GPT, to synthesize 3D samples based on a single 2D slice from the perspective of video generation. The VQ-VAE is utilized to compress high-dimensional input video, i.e., the sequence of continuous rock slices, to discrete latent codes and reconstruct them. In order to obtain diverse reconstructions, the discrete latent codes are modeled using conditional GPT in an autoregressive manner, while incorporating conditional information from a given slice, rock type, and porosity. We conduct two experiments on five kinds of rocks, and the results demonstrate that RockGPT can produce different kinds of rocks with the same model, and the reconstructed samples can successfully meet certain specified porosities. In a broader sense, through leveraging the proposed conditioning scheme, RockGPT constitutes an effective way to build a general model to produce multiple kinds of rocks simultaneously that also satisfy user-defined properties.

eess.IV↗

arXiv2021

High Entropy Oxide Relaxor Ferroelectrics

Relaxor ferrolectrics are important in technological applications due to a strong electromechanical response, energy storage capacity, electrocaloric effect, and pyroelectric energy conversion properties. Current efforts to discover and design new materials in this class generally rely on substitutional doping of known ferroelectrics, as slight changes to local compositional order can significantly affect the Curie temperature, morphotropic phase boundary, and electromechanical responses. In this work, we demonstrate that moving to the strong limit of compositional complexity in an ABO3 perovskite allows stabilization of novel relaxor responses that do not rely on a single narrow phase transition region. Entropy-assisted synthesis approaches are used to create single crystal Ba(Ti0.2Sn0.2Zr0.2Hf0.2Nb0.2)O3 [Ba(5B)O] films. The high levels of configurational disorder present in this system is found to influence dielectric relaxation, phase transitions, nano-polar domain formation, and Curie temperature. Temperature-dependent dielectric, Raman spectroscopy and second-harmonic generation measurements reveal multiple phase transitions, a high Curie temperature of 570 K, and the relaxor ferroelectric nature of Ba(5B)O films. The first principles theory calculations are used to predict possible combinations of cations to quantify the relative feasibility of formation of highly disordered single-phase perovskite systems. The ability to stabilize single-phase perovskites with such a large number of different cations on the B-sites offers new possibilities for designing high-performance materials for piezoelectric, pyroelectric and tunable dielectric applications.

cond-mat.mtrl-sci↗

arXiv2021

Digital rock reconstruction with user-defined properties using conditional generative adversarial networks

Uncertainty is ubiquitous with flow in subsurface rocks because of their inherent heterogeneity and lack of in-situ measurements. To complete uncertainty analysis in a multi-scale manner, it is a prerequisite to provide sufficient rock samples. Even though the advent of digital rock technology offers opportunities to reproduce rocks, it still cannot be utilized to provide massive samples due to its high cost, thus leading to the development of diversified mathematical methods. Among them, two-point statistics (TPS) and multi-point statistics (MPS) are commonly utilized, which feature incorporating low-order and high-order statistical information, respectively. Recently, generative adversarial networks (GANs) are becoming increasingly popular since they can reproduce training images with excellent visual and consequent geologic realism. However, standard GANs can only incorporate information from data, while leaving no interface for user-defined properties, and thus may limit the representativeness of reconstructed samples. In this study, we propose conditional GANs for digital rock reconstruction, aiming to reproduce samples not only similar to the real training data, but also satisfying user-specified properties. In fact, the proposed framework can realize the targets of MPS and TPS simultaneously by incorporating high-order information directly from rock images with the GANs scheme, while preserving low-order counterparts through conditioning. We conduct three reconstruction experiments, and the results demonstrate that rock type, rock porosity, and correlation length can be successfully conditioned to affect the reconstructed rock images. Furthermore, in contrast to existing GANs, the proposed conditioning enables learning of multiple rock types simultaneously, and thus invisibly saves computational cost.

cs.CV↗

arXiv2021

Site Mixing for Engineering Magnetic Topological Insulators

The van der Waals compound, MnBi$_2$Te$_4$, is the first intrinsic magnetic topological insulator, providing a materials platform for exploring exotic quantum phenomena such as the axion insulator state and the quantum anomalous Hall effect. However, intrinsic structural imperfections lead to bulk conductivity, and the roles of magnetic defects are still unknown. With higher concentrations of same types of magnetic defects, the isostructural compound MnSb$_2$Te$_4$ is a better model system for a systematic investigation of the connections among magnetic, topology and lattice defects. In this work, the impact of antisite defects on the magnetism and electronic structure is studied in MnSb$_2$Te$_4$. Mn-Sb site mixing leads to complex magnetic structures and tunes the interlayer magnetic coupling between antiferromagnetic and ferromagnetic. The detailed nonstoichiometry and site-mixing of MnSb$_2$Te$_4$ crystals depend on the growth parameters, which can lead to $\approx$40\% of Mn sites occupied by Sb and $\approx$15\% of Sb sites by Mn in as-grown crystals. Single crystal neutron diffraction and electron microscopy studies show nearly random distribution of the antisite defects. Band structure calculations suggest that the Mn-Sb site-mixing favors a FM interlayer coupling, consistent with experimental observation, but is detrimental to the band inversion required for a nontrivial topology. Our results suggest a long range magnetic order of Mn ions sitting on Bi sites in MnBi$_2$Te$_4$. The effects of site mixing should be considered in all layered heterostructures that consist of alternating magnetic and topological layers, including the entire family of MnTe(Bi$_2$Te$_3$)$_n$, its Sb analogs and their solid solution.

cond-mat.mtrl-sci↗

arXiv2020

Using Deep Learning to Improve Ensemble Smoother: Applications to Subsurface Characterization

Ensemble smoother (ES) has been widely used in various research fields to reduce the uncertainty of the system-of-interest. However, the commonly-adopted ES method that employs the Kalman formula, that is, ES$_\text{(K)}$, does not perform well when the probability distributions involved are non-Gaussian. To address this issue, we suggest to use deep learning (DL) to derive an alternative update scheme for ES in complex data assimilation applications. Here we show that the DL-based ES method, that is, ES$_\text{(DL)}$, is more general and flexible. In this new update scheme, a high volume of training data are generated from a relatively small-sized ensemble of model parameters and simulation outputs, and possible non-Gaussian features can be preserved in the training data and captured by an adequate DL model. This new variant of ES is tested in two subsurface characterization problems with or without Gaussian assumptions. Results indicate that ES$_\text{(DL)}$ can produce similar (in the Gaussian case) or even better (in the non-Gaussian case) results compared to those from ES$_\text{(K)}$. The success of ES$_\text{(DL)}$ comes from the power of DL in extracting complex (including non-Gaussian) features and learning nonlinear relationships from massive amounts of training data. Although in this work we only apply the ES$_\text{(DL)}$ method in parameter estimation problems, the proposed idea can be conveniently extended to analysis of model structural uncertainty and state estimation in real-time forecasting studies.

math.OC↗

arXiv2020

Optimal unidirectional amplification induced by optical gain in optomechanical systems

We propose a three-mode optomechanical system to realize optical nonreciprocal transmission with unidirectional amplification, where the system consists of two coupled cavities and one mechanical resonator which interacts with only one of the cavities. Additionally, the optical gain is introduced into the optomechanical cavity. It is found that for a strong optical input, the optical transmission coefficient can be greatly amplified in a particular direction and suppressed in the opposite direction. The expressions of the optimal transmission coefficient and the corresponding isolation ratio are given analytically. Our results pave a way to design high-quality nonreciprocal devices based on optomechanical systems.

quant-ph↗

arXiv2020

Surrogate-Based Bayesian Inverse Modeling of the Hydrological System: An Adaptive Approach Considering Surrogate Approximation Error

Bayesian inverse modeling is important for a better understanding of hydrological processes. However, this approach can be computationally demanding, as it usually requires a large number of model evaluations. To address this issue, one can take advantage of surrogate modeling techniques. Nevertheless, when approximation error of the surrogate model is neglected, the inversion result will be biased. In this paper, we develop a surrogate-based Bayesian inversion framework that explicitly quantifies and gradually reduces the approximation error of the surrogate. Specifically, two strategies are proposed to quantify the surrogate error. The first strategy works by quantifying the surrogate prediction uncertainty with a Bayesian method, while the second strategy uses another surrogate to simulate and correct the approximation error of the primary surrogate. By adaptively refining the surrogate over the posterior distribution, we can gradually reduce the surrogate approximation error to a small level. Demonstrated with three case studies involving high dimensionality, multimodality, and a real-world application, it is found that both strategies can reduce the bias introduced by surrogate approximation error, while the second strategy that integrates two methods (i.e., polynomial chaos expansion and Gaussian process in this work) that complement each other shows the best performance.

stat.CO↗

arXiv2019

Physics-informed semantic inpainting: Application to geostatistical modeling

A fundamental problem in geostatistical modeling is to infer the heterogeneous geological field based on limited measurements and some prior spatial statistics. Semantic inpainting, a technique for image processing using deep generative models, has been recently applied for this purpose, demonstrating its effectiveness in dealing with complex spatial patterns. However, the original semantic inpainting framework incorporates only information from direct measurements, while in geostatistics indirect measurements are often plentiful. To overcome this limitation, here we propose a physics-informed semantic inpainting framework, employing the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and jointly incorporating the direct and indirect measurements by exploiting the underlying physical laws. Our simulation results for a high-dimensional problem with 512 dimensions show that in the new method, the physical conservation laws are satisfied and contribute in enhancing the inpainting performance compared to using only the direct measurements.

eess.IV↗

arXiv2019

Synthesis, magnetization and heat capacity of triangular lattice materials NaErSe$_2$ and KErSe$_2$

In this paper we report the synthesis, magnetization and heat capacity of the frustrated magnets \emph{A}ErSe$_2$(\emph{A}=Na,K) which contain perfect triangular lattices of Er$^{3+}$. The magnetization data suggests no long-range magnetic order exists in \emph{A}ErSe$_2$(\emph{A}=Na,K), which is consistent with the heat capacity measurements. Large anisotropy is observed between the magnetization within the \emph{ab} plane and along the \emph{c} axis of both compounds. When the magnetic field is applied along \emph{ab} plane, anomalies are observed at 1.8 $μ_B$ in NaErSe$_2$ at 0.2 T and 2.1 $μ_B$ in KErSe$_2$ at 0.18 T. Unlike NaErSe$_2$, a plateau-like field-induced metamagnetic transition is observed for H$\|$\emph{c} below 1 K in KErSe$_2$. Two broad peaks are observed in the heat capacity below 10 K indicating possible crystal electric field(CEF) effects and magnetic entropy released under different magnetic fields. All results indicate that \emph{A}ErSe$_2$ are strongly anisotropic, frustrated magnets with field-induced transition at low temperature. The lack of signatures for long-range magnetic order implies that these materials are candidates for hosting a quantum spin liquid ground state.

cond-mat.mtrl-sci↗

arXiv2019

Magnetic anisotropy in single crystal high entropy perovskite oxide La(Cr0.2Mn0.2Fe0.2Co0.2Ni0.2)O3 films

Local configurational disorder can have a dominating role in the formation of macroscopic functional responses in strongly correlated materials. Here, we use entropy-stabilization synthesis to create single crystal epitaxial ABO3 perovskite thin films with equal atomic concentration of 3d transition metal cations on the B-site sublattice. X-ray diffraction, atomic force microscopy, and scanning transmission electron microscopy of La(Cr0.2Mn0.2Fe0.2Co0.2Ni0.2)O3 (L5BO) films demonstrate excellent crystallinity, smooth film surfaces, and uniform mixing of the 3d transition metal cations throughout the B-site sublattice. The magnetic properties are strongly dependent on substrate-induced lattice anisotropy and suggest the presence of long-range magnetic order in these exceptionally disordered materials. The ability to populate multiple elements onto a single sublattice in complex crystal structures opens new possibilities to design functionality in correlated systems and enable novel fundamental studies seeking to understand how diverse local bonding environments can work to generate macroscopic responses, such as those driven by electron-phonon channels and complex exchange interaction pathways.

cond-mat.str-el↗

arXiv2019

Self-Assembled Room Temperature Multiferroic BiFeO3-LiFe5O8 Nanocomposites

Multiferroic materials have driven significant research interest due to their promising technological potential. Developing new room-temperature multiferroics and understanding their fundamental properties are important to reveal unanticipated physical phenomena and potential applications. Here, a new room temperature multiferroic nanocomposite comprised of an ordered ferrimagnetic spinel LiFe5O8 (LFO) and a ferroelectric perovskite BiFeO3 (BFO) is presented. We observed that lithium (Li)-doping in BFO favors the formation of LFO spinel as a secondary phase during the synthesis of LixBi1-xFeO3 nanoceramics. Multimodal functional and chemical imaging methods are used to map the relationship between doping-induced phase separation and local ferroic properties in both the BFO-LFO composite ceramics and self-assembled nanocomposite thin films. The energetics of phase separation in Li doped BFO and the formation of BFO-LFO composites is supported by first principles calculations. These findings shed light on Li-ion role in the formation of a functionally important room temperature multiferroic and open a new approach in the synthesis of light element doped nanocomposites.

cond-mat.mtrl-sci↗

arXiv2019

Diffractive imaging of dissociation and ground state dynamics in a complex molecule

We have investigated the structural dynamics in photoexcited 1,2-diiodotetrafluoroethane molecules (C2F4I2) in the gas phase experimentally using ultrafast electron diffraction and theoretically using FOMO-CASCI excited state dynamics simulations. The molecules are excited by an ultra-violet femtosecond laser pulse to a state characterized by a transition from the iodine 5p orbital to a mixed 5p|| hole and CF2 antibonding orbital, which results in the cleavage of one of the carbon-iodine bonds. We have observed, with sub-Angstrom resolution, the motion of the nuclear wavepacket of the dissociating iodine atom followed by coherent vibrations in the electronic ground state of the C2F4I radical. The radical reaches a stable classical (non-bridged) structure in less than 200 fs.

physics.chem-ph↗

arXiv2019

Nanoscale Interlayer Defects in Iron Arsenides

Using a local real-space microscopy probe, we discover evidence of nanoscale interlayer defects along the c-crystallographic direction in BaFe2As2 (122) based iron-arsenide superconductors. We find ordered 122 atomic arrangements within the ab-plane, and within regions of ~10 to 20 nm size perpendicular to this plane. While the FeAs substructure is very rigid, Ba ions are relatively weakly bound and can be displaced from the 122, forming stacking faults resulting in the physical separation of the 122 between adjacent ordered domains. The evidence for interlayer defects between the FeAs superconducting planes gives perspective on the minimal connection between interlayer chemical disorder and high-temperature superconductivity. In particular, the Cooper pairs may be finding a way around such localized interlayer defects through a percolative path of the ordered layered 122 lattice that may not affect Tc.

cond-mat.supr-con↗