arXiv subjects

Qiang Zhu

Publications and source records attributed to Qiang Zhu.

At least 91 records · Page 5Linked to original sources

arXiv2022

Impacts of f-d Kondo cloud on superconductivity of nickelates

The discovery of superconducting nickelates reignited hope for elucidating the high-$T_{\textrm{c}}$ superconductivity mechanism in the isostructural cuprates. While in the cuprates, the superconducting gap opens up on a single-band of the quasi-2D Fermi surface, the nickelates are known to have 3D nature of electronic structure with multi-band. This raises a serious question about the role of 2D nature for the high-$T_{\textrm{c}}$ superconductivity. Here, employing dynamical mean field theory combined with GW method, we found the Kondo effect driven by the strong correlation of Nd-4$f$ and Ni-3$d$ electrons emerging at low temperature. The Kondo effect modifies the topology of the Fermi surface leading to 3D multi-band nature. Remarkably, the Kondo effect is easily destroyed by lattice modulation, leading to the quasi-2D nature. Our findings clearly explain the inconsistent occurrence of superconductivity and distinct electrical resistivity behavior between NdNiO$_{2}$ bulk and films.

cond-mat.str-el↗

arXiv2022

Short-range order and its impacts on the BCC NbMoTaW multi-principal element alloy by the machine-learning potential

We employ a machine-learning force field, trained by a neural network (NN) with bispectrum coefficients as descriptors, to investigate the short-range order (SRO) influences on the BCC NbMoTaW alloy strengthening mechanism. The NN interatomic potential provides a transferable force field with density functional theory accuracy. This novel NN potential is applied to elucidate the SRO effects on the elasticity, vibrational modes, plasticity, and strength in the NbMoTaW multi-principal element alloy (MPEA). The results show the strong attraction among Mo-Ta pairs forming the local ordered B2 structures, which could be tuned via temperature and improved by Nb content. SRO increases the elastic constants and high-frequency phonon modes as well as introduces extra lattice friction of dislocation motion. This approach enables a rapid compositional screening, paves the way for computation-guided materials design of new MPEAs with better performance, and opens avenues for tuning the mechanical properties by processing optimization.

cond-mat.mtrl-sci↗

arXiv2022

Size-Dependent Nucleation in Crystal Phase Transition from Machine Learning Metadynamics

In this work, we present an efficient framework that combines machine learning potential (MLP) and metadynamics to explore multi-dimensional free energy surfaces for investigating solid-solid phase transition. Based on the spectral descriptors and neural networks regression, we have developed a computationally scalable MLP model to warrant an accurate interpolation of the energy surface where two phases coexist. Applying the framework to the metadynamics simulation of B4-B1 phase transition of GaN under 50 GPa with different model sizes, we observe the sequential change of phase transition mechanism from collective modes to nucleation and growths. When the system size is at or below 128 000 atoms, the nucleation and growth appear to follow a preferred direction. At larger sizes, the nucleation tends to occur at multiple sites simultaneously and grow to microstructures by passing the critical size. The observed change of atomistic mechanism manifests the importance of statistical sampling with large system size. The combination of MLP and metadynamics is likely to be applicable to a broad class of induced reconstructive phase transitions at extreme conditions.

cond-mat.mtrl-sci↗

arXiv2021

Learning Decomposed Representation for Counterfactual Inference

The fundamental problem in treatment effect estimation from observational data is confounder identification and balancing. Most of the previous methods realized confounder balancing by treating all observed pre-treatment variables as confounders, ignoring further identifying confounders and non-confounders. In general, not all the observed pre-treatment variables are confounders that refer to the common causes of the treatment and the outcome, some variables only contribute to the treatment and some only contribute to the outcome. Balancing those non-confounders, including instrumental variables and adjustment variables, would generate additional bias for treatment effect estimation. By modeling the different causal relations among observed pre-treatment variables, treatment and outcome, we propose a synergistic learning framework to 1) identify confounders by learning decomposed representations of both confounders and non-confounders, 2) balance confounder with sample re-weighting technique, and simultaneously 3) estimate the treatment effect in observational studies via counterfactual inference. Empirical results on synthetic and real-world datasets demonstrate that the proposed method can precisely decompose confounders and achieve a more precise estimation of treatment effect than baselines.

stat.ME↗

arXiv2021

The role of surface spin polarization on ceria-supported Pt nanoparticles

In this work, we employ first-principles simulations to investigate the spin polarization of CeO$_2$-(111) surface and its impact on interactions between a ceria support and Pt nanoparticles. For the first time, we report that the CeO$_2$-(111) surface exhibits a robust surface spin polarization due to the internal charge transfer between atomic Ce and O layers. In turn, it can lower the surface oxygen vacancy formation energy and enhance the oxide reducibility. We show that the inclusion of spin polarization can therefore significantly reduce the major activation barrier in the proposed reaction pathway of CO oxidation on ceria-supported Pt nanoparticles. For metal-support interactions, surface spin polarization enhances the bonding between Pt nanoparticle and ceria surface oxygen, while CO adsorption on Pt nanoparticles weakens the interfacial interaction regardless of spin polarization.

cond-mat.mtrl-sci↗

arXiv2021

Multi-scale Information Assembly for Image Matting

Image matting is a long-standing problem in computer graphics and vision, mostly identified as the accurate estimation of the foreground in input images. We argue that the foreground objects can be represented by different-level information, including the central bodies, large-grained boundaries, refined details, etc. Based on this observation, in this paper, we propose a multi-scale information assembly framework (MSIA-matte) to pull out high-quality alpha mattes from single RGB images. Technically speaking, given an input image, we extract advanced semantics as our subject content and retain initial CNN features to encode different-level foreground expression, then combine them by our well-designed information assembly strategy. Extensive experiments can prove the effectiveness of the proposed MSIA-matte, and we can achieve state-of-the-art performance compared to most existing matting networks.

cs.CV↗

arXiv2021

Cross-domain Joint Dictionary Learning for ECG Inference from PPG

The inverse problem of inferring electrocardiogram (ECG) from photoplethysmogram (PPG) is an emerging research direction that combines the easy measurability of PPG and the rich clinical knowledge of ECG for long-term continuous cardiac monitoring. The prior art for reconstruction using a universal basis has limited fidelity for uncommon ECG waveform shapes due to the lack of rich representative power. In this paper, we design two dictionary learning frameworks, the cross-domain joint dictionary learning (XDJDL) and the label-consistent XDJDL (LC-XDJDL), to further improve the ECG inference quality and enrich the PPG-based diagnosis knowledge. Building on the K-SVD technique, our proposed joint dictionary learning frameworks aim to maximize the expressive power by optimizing simultaneously a pair of signal dictionaries for PPG and ECG with the transforms to relate their sparse codes and disease information. The proposed models are evaluated with 34,000+ ECG/PPG cycle pairs containing a variety of ECG morphologies and cardiovascular diseases. We demonstrate both visually and quantitatively that our proposed frameworks can achieve better inference performance than previous methods, suggesting an encouraging potential for ECG screening using PPG based on the proactive learned PPG-ECG relationship.

eess.SP↗

arXiv2020

PyXtal: a Python Library for Crystal Structure Generation and Symmetry Analysis

We present PyXtal, a new package based on the Python programming language, used to generate structures with specific symmetry and chemical compositions for both atomic and molecular systems. This soft ware provides support for various systems described by point, rod, layer, and space group symmetries. With only the inputs of chemical composition and symmetry group information, PyXtal can automatically find a suitable combination of Wyckoff positions with a step-wise merging scheme. Further, when the molecular geometry is given, PyXtal can generate different dimensional organic crystals with molecules occupying both general and special Wyckoff positions. Optionally, PyXtal also accepts user-defined parameters (e.g., cell parameters, minimum distances and Wyckoff positions). In general, PyXtal serves three purposes: (1) to generate custom structures, (2) to modulate the structure by symmetry relations, (3) to interface the existing structure prediction codes that require the generation of random symmetric structures. In addition, we provide several utilities that facilitate the analysis of structures, including symmetry analysis, geometry optimization, and simulations of powder X-ray diffraction (XRD). Full documentation of PyXtal is available at \url{https://pyxtal.readthedocs.io}.

cond-mat.mtrl-sci↗

arXiv2020

Adaptive Multi-Trace Carving for Robust Frequency Tracking in Forensic Applications

In the field of information forensics, many emerging problems involve a critical step that estimates and tracks weak frequency components in noisy signals. It is often challenging for the prior art of frequency tracking to i)achieve a high accuracy under noisy conditions, ii)detect and track multiple frequency components efficiently, or iii)strike a good trade-off of the processing delay versus the resilience and the accuracy of tracking. To address these issues, we propose Adaptive Multi-Trace Carving (AMTC), a unified approach for detecting and tracking one or more subtle frequency components under very low signal-to-noise ratio (SNR) conditions and in near real time. AMTC takes as input a time-frequency representation of the system's preprocessing results (such as the spectrogram), and identifies frequency components through iterative dynamic programming and adaptive trace compensation. The proposed algorithm considers relatively high energy traces sustaining over a certain duration as an indicator of the presence of frequency/oscillation components of interest and track their time-varying trend. Extensive experiments using both synthetic data and real-world forensic data of power signatures and physiological monitoring reveal that the proposed method outperforms representative prior art under low SNR conditions, and can be implemented in near real-time settings. The proposed AMTC algorithm can empower the development of new information forensic technologies that harness very small signals.

eess.SP↗

arXiv2020

Switchable Atomically Thin 2D Electrides from First-principles Prediction

Electrides, with excess anionic electrons confined in their empty space, are promising for uses in catalysis, nonlinear optics and spin-electronics. However, the application of electrides is limited by their high chemical reactivity with the environmental agents. In this work, we report the discovery of a group of two-dimensional (2D) moonolayer electrides with the presence of switchable nearly free electron (NFE) states in their electronic structures. Unlike conventional electrides, which are metals with floating electrons forming the partially occupied bands close to the Fermi level, the switchable electrides are chemically much less active semiconductors holding the NFE states that are 0.3-1.5 eV above the Fermi level. According to a high throughput search, we identified 12 2D candidates that possess such low-energy NFE states. Among them, 11 2D materials can likely be exfoliated from the known layered materials. Under external forces, such as a compressive strain, these NFE states stemming from the surface image potential will be pushed downward to cross the Fermi level. Remarkably, the critical semiconductor-metal transition can be achieved by a strain as low as 3% in 2D monolayer Na$_2$Pd$_3$O$_4$. As such, the switchable 2D electrides may provide an ideal platform for exploring novel quantum phenomena and modern electronic device applications.

cond-mat.mtrl-sci↗

arXiv2020

PyXtal FF: a Python Library for Automated Force Field Generation

We present PyXtal FF, a package based on Python programming language, for developing machine learning potentials (MLPs). The aim of PyXtal FF is to promote the application of atomistic simulations by providing several choices of structural descriptors and machine learning regressions in one platform. Based on the given choice of structural descriptors (including the atom-centered symmetry functions, embedded atom density, SO4 bispectrum, and smooth SO3 power spectrum), PyXtal FF can train the MLPs with either the generalized linear regression or neural networks model, by simultaneously minimizing the errors of energy/forces/stress tensors in comparison with the data from the ab-initio simulation. The trained MLP model from PyXtal FF is interfaced with the Atomic Simulation Environment (ASE) package, which allows different types of light-weight simulations such as geometry optimization, molecular dynamics simulation, and physical properties prediction. Finally, we will illustrate the performance of PyXtal FF by applying it to investigate several material systems, including the bulk SiO2, high entropy alloy NbMoTaW, and elemental Pt for general purposes. Full documentation of PyXtal FF is available at https://pyxtal-ff.readthedocs.io.

physics.comp-ph↗

arXiv2020

Spectral Neural Network Potentials for Binary Alloys

In this work, we present a numerical implementation to compute the atom centered descriptors introduced by Bartok et al (Phys. Rev. B, 87, 184115, 2013) based on the harmonic analysis of the atomic neighbor density function. Specifically, we focus on two types of descriptors, the smooth SO(3) power spectrum with the explicit inclusion of a radial basis and the SO(4) bispectrum obtained through mapping the radial component onto a polar angle of a four dimensional hypersphere. With these descriptors, various interatomic potentials for binary Ni-Mo alloys are obtained based on linear and neural network regression models. Numerical experiments suggest that both descriptors produce similar results in terms of accuracy. For linear regression, the smooth SO(3) power spectrum is superior to the SO(4) bispectrum when a large band limit is used. In neural network regression, a better accuracy can be achieved with even less number of expansion components for both descriptors. As such, we demonstrate that spectral neural network potentials are feasible choices for large scale atomistic simulation.

physics.comp-ph↗

arXiv2020

Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis

Infectious keratitis is the most common entities of corneal diseases, in which pathogen grows in the cornea leading to inflammation and destruction of the corneal tissues. Infectious keratitis is a medical emergency, for which a rapid and accurate diagnosis is needed for speedy initiation of prompt and precise treatment to halt the disease progress and to limit the extent of corneal damage; otherwise it may develop sight-threatening and even eye-globe-threatening condition. In this paper, we propose a sequential-level deep learning model to effectively discriminate the distinction and subtlety of infectious corneal disease via the classification of clinical images. In this approach, we devise an appropriate mechanism to preserve the spatial structures of clinical images and disentangle the informative features for clinical image classification of infectious keratitis. In competition with 421 ophthalmologists, the performance of the proposed sequential-level deep model achieved 80.00% diagnostic accuracy, far better than the 49.27% diagnostic accuracy achieved by ophthalmologists over 120 test images.

eess.IV↗

arXiv2020

Computation and data driven discovery of topological phononic materials

The discovery of topological quantum states marks a new chapter in both condensed matter physics and materials sciences. By analogy to spin electronic system, topological concepts have been extended into phonons, boosting the birth of topological phononics (TPs). Here, we present a high-throughput screening and data-driven approach to compute and evaluate TPs among over 10,000 materials. We have clarified 5014 TP materials and classified them into single Weyl, high degenerate Weyl, and nodal-line (ring) TPs. Among them, three representative cases of TPs have been discussed in detail. Furthermore, we suggest 322 TP materials with potential clean nontrivial surface states, which are favorable for experimental characterizations. This work significantly increases the current library of TP materials, which enables an in-depth investigation of their structure-property relations and opens new avenues for future device design related to TPs.

cond-mat.mtrl-sci↗

arXiv2020

Neural Networks Potential from the Bispectrum Component: A Case Study on Crystalline Silicon

In this article, we present a systematic study in developing machine learning force fields (MLFF) for crystalline silicon. While the main-stream approach of fitting a MLFF is to use a small and localized training sets from molecular dynamics simulation, it is unlikely to cover the global feature of the potential energy surface. To remedy this issue, we used randomly generated symmetrical crystal structures to train a more general Si-MLFF. Further, we performed substantial benchmarks among different choices of materials descriptors and regression techniques on two different sets of silicon data. Our results show that neural network potential fitting with bispectrum coefficients as the descriptor is a feasible method for obtaining accurate and transferable MLFF.

physics.comp-ph↗

arXiv2020

Old Story New Tell: The Graphite to Diamond Transition Revisited

Graphite and diamond are two well-known allotropes of carbon with distinct physical properties due to different atomic connectivity. Graphite has a layered structure in which the honeycomb carbon sheets can easily glide, while atoms in diamond are strongly bonded in all three dimensions. The transition from graphite to diamond has been a central subject in physical science. One way to turn graphite into diamond is to apply the high pressure and high temperature (HPHT) conditions. However, atomistic mechanism of this transition is still under debate. From a series of large-scale molecular dynamics (MD) simulations, we report a mechanism that the diamond nuclei originate at the graphite grain boundaries and propagate in two preferred directions. In addition to the widely accepted [001] direction, we found that the growth along [120] direction of graphite is even faster. In this scenario, cubic diamond (CD) is the kinetically favorable product, while hexagonal diamond (HD) would appear as minor amounts of twinning structures in two main directions. Following the crystallographic orientation relationship, the coherent interface t-(100)gr//(11-1)cd + [010]gr//[1-10]cd was also confirmed by high-resolution transmission electron microscopy (HR-TEM) experiment. The proposed phase transition mechanism does not only reconcile the longstanding debate regarding the role of HD in graphite-diamond transition, but also yields the atomistic insight into microstructure engineering via controlled solid phase transition.

cond-mat.mtrl-sci↗

arXiv2019

High Dielectric Ternary Oxides from Crystal Structure Prediction and High-throughput Screening

The development of new high dielectric materials is essential for advancement in modern electronics. Oxides are generally regarded as the most promising class of high dielectric materials for industrial applications as they possess both high dielectric constants and large band gaps. Most previous researches on high dielectrics were limited to already known materials. In this study, we conducted an extensive search for high dielectrics over a set of ternary oxides by combining crystal structure prediction and density functional perturbation theory calculations. From this search, we adopted multiple stage screening to identify 440 new low-energy high dielectric materials. Among these materials, 33 were identified as potential high dielectrics favorable for modern device applications. Our research has opened an avenue to explore novel high dielectric materials by combining crystal structure prediction and high throughput screening.

cond-mat.mtrl-sci↗

arXiv2019

Respiratory Rate Estimation from Face Videos

Vital signs, such as heart rate (HR), heart rate variability (HRV), respiratory rate (RR), are important indicators for a person's health. Vital signs are traditionally measured with contact sensors, and may be inconvenient and cause discomfort during continuous monitoring. Commercial cameras are promising contact-free sensors, and remote photoplethysmography (rPPG) have been studied to remotely monitor heart rate from face videos. For remote RR measurement, most prior art was based on small periodical motions of chest regions caused by breathing cycles, which are vulnerable to subjects' voluntary movements. This paper explores remote RR measurement based on rPPG obtained from face videos. The paper employs motion compensation, two-phase temporal filtering, and signal pruning to capture signals with high quality. The experimental results demonstrate that the proposed framework can obtain accurate RR results and can provide HR, HRV and RR measurement synergistically in one framework.

eess.IV↗