arXiv subjects

Siyuan Zhang

Publications and source records attributed to Siyuan Zhang.

At least 91 records · Page 5Linked to original sources

arXiv2023

Direct Motif Extraction from High Resolution Crystalline STEM Images

During the last decade, automatic data analysis methods concerning different aspects of crystal analysis have been developed, e.g., unsupervised primitive unit cell extraction and automated crystal distortion and defects detection. However, an automatic, unsupervised motif extraction method is still not widely available yet. Here, we propose and demonstrate a novel method for the automatic motif extraction in real space from crystalline images based on a variational approach involving the unit cell projection operator. Due to the non-convex nature of the resulting minimization problem, a multi-stage algorithm is used. First, we determine the primitive unit cell in form of two lattice vectors. Second, a motif image is estimated using the unit cell information. Finally, the motif is determined in terms of atom positions inside the unit cell. The method was tested on various synthetic and experimental HAADF STEM images. The results are a representation of the motif in form of an image, atomic positions, primitive unit cell vectors, and a denoised and a modeled reconstruction of the input image. The method was applied to extract the primitive cells of complex $μ$-phase structures Nb$_\text{6.4}$Co$_\text{6.6}$ and Nb$_\text{7}$Co$_\text{6}$, where subtle differences between their interplanar spacings were determined.

eess.IV↗

arXiv2023

Tailoring the plasticity of topologically close-packed phases via the crystals' fundamental building blocks

Brittle topologically close-packed precipitates form in many advanced alloys. Due to their complex structures little is known about their plasticity. Here, we present a strategy to understand and tailor the deformability of these complex phases by considering the Nb-Co μ-phase as an archetypal material. The plasticity of the Nb-Co μ-phase is controlled by the Laves phase building block that forms parts of its unit cell. We find that between the bulk C15-NbCo$_2$ Laves and Nb-Co μ-phase, the interplanar spacing and local elastic modulus of the Laves phase building block change, leading to a strong reduction in hardness and elastic modulus, as well as a transition from synchroshear to crystallographic slip. Furthermore, as the composition changes from Nb$_6$Co$_7$ to Nb$_7$Co$_6$, the Co atoms in the triple layer are substituted such that the triple layer of the Laves phase building block becomes a slab of pure Nb, resulting in inhomogeneous changes in elasticity and a transition from crystallographic slip to a glide-and-shuffle mechanism. These findings open opportunities to purposefully tailor the plasticity of these topologically close-packed phases in bulk, but at the atomic scale of interplanar spacing and local shear modulus of the fundamental crystal building blocks in their large unit cells.

cond-mat.mtrl-sci↗

arXiv2023

NEPHELE: A Neural Platform for Highly Realistic Cloud Radiance Rendering

We have recently seen tremendous progress in neural rendering (NR) advances, i.e., NeRF, for photo-real free-view synthesis. Yet, as a local technique based on a single computer/GPU, even the best-engineered Instant-NGP or i-NGP cannot reach real-time performance when rendering at a high resolution, and often requires huge local computing resources. In this paper, we resort to cloud rendering and present NEPHELE, a neural platform for highly realistic cloud radiance rendering. In stark contrast with existing NR approaches, our NEPHELE allows for more powerful rendering capabilities by combining multiple remote GPUs and facilitates collaboration by allowing multiple people to view the same NeRF scene simultaneously. We introduce i-NOLF to employ opacity light fields for ultra-fast neural radiance rendering in a one-query-per-ray manner. We further resemble the Lumigraph with geometry proxies for fast ray querying and subsequently employ a small MLP to model the local opacity lumishperes for high-quality rendering. We also adopt Perfect Spatial Hashing in i-NOLF to enhance cache coherence. As a result, our i-NOLF achieves an order of magnitude performance gain in terms of efficiency than i-NGP, especially for the multi-user multi-viewpoint setting under cloud rendering scenarios. We further tailor a task scheduler accompanied by our i-NOLF representation and demonstrate the advance of our methodological design through a comprehensive cloud platform, consisting of a series of cooperated modules, i.e., render farms, task assigner, frame composer, and detailed streaming strategies. Using such a cloud platform compatible with neural rendering, we further showcase the capabilities of our cloud radiance rendering through a series of applications, ranging from cloud VR/AR rendering.

cs.GR↗

arXiv2023

Revealing compositional evolution of PdAu electrocatalyst by atom probe tomography

Pd-based electro-catalysts are a key component to improve the methanol oxidation reaction (MOR) kinetics from alcohol fuel cells. However, the performance of such catalysts is degraded over time. To understand the microstructural/atomic scale chemical changes responsible for such effect, scanning (transmission) electron microscopies and atom probe tomography (APT) were performed after accelerated degradation tests (ADT). No morphological changes are observed after 1000 MOR cycles. Contrastingly, (1) Pd and B are leached from PdAu nanoparticles and (2) Au-rich regions are formed at the surface of the catalyst. These insights highlight the importance of understanding the chemical modification undergoing upon MOR to design superior catalysts.

cond-mat.mtrl-sci↗

arXiv2022

Chemical Partitioning at Crystalline Defects in PtAu as a Pathway to Stabilize Electrocatalysts

Dissolution of electrocatalysts during long-term and dynamic operation is a challenging problem in energy conversion and storage devices such as fuel cells and electrolyzers. To develop stable electrocatalysts, we adopt the design concept of segregation engineering, which uses solute segregation prone to electrochemical dissolution at internal defects, i.e., grain boundaries and dislocations. We showcase the feasibility of this approach by stabilizing a model Pt catalyst with an addition of more noble Au (approximately 5 atomic percent). We characterized the defects' nanoscale structure and chemistry, and monitored the electrochemical dissolution of Pt and PtAu alloys by online inductively coupled plasma mass spectrometry. Once segregated to defects, Au atoms can stabilize and hence passivate the most vulnerable sites against electrochemical dissolution and improve the stability and longevity of the Pt electrocatalysts by more than an order of magnitude. This opens pathways to use solute segregation to defects for the development of more stable nanoscale electrocatalysts, a concept applicable for a wide range of catalytic systems.

cond-mat.mtrl-sci↗

arXiv2022

Preferred corrosion pathways for oxygen in Al2Ca-twin boundaries and dislocations

With an ongoing discussion on the oxygen diffusion along crystal defects remaining, it is difficult to study this phenomenon in Al containing intermetallic materials due to its rapid and passivating oxide formation. We report here the observation of enhanced oxygen diffusion along crystal defects, i.e. dislocations and twin boundaries, in the C15 Al 2 Ca Laves phase and how the presence of oxygen induces structural changes at these defects. Three main phases were identified and characterized structurally by aberration-corrected, atomic resolution scanning transmission electron microscopy, analytically by energy dispersive X-ray spectroscopy and electron energy loss spectroscopy. Unlike the C15 bulk phase, the twin boundary and dislocation transformed into a few nanometer wide amorphous phase, which depletes in Al and Ca but is highly enriched in oxygen. The dislocation even shows coexistence of the amorphous phase with a simple Al-rich A1 fcc phase. This A1 phase only depletes in Ca, not in Al (Al remains at bulk concentration), and is also enriched in oxygen. The Al-rich A1 phase is coherent with the C15 matrix. Electron energy loss spectroscopy revealed the amorphous phase to be Al 2 O 3 . We thereby show as one of the first studies that oxygen diffusion along crystal defects, especially also at the twin boundary can induce the formation of an amorphous oxide along themselves. The identification of oxygen-induced transformation at strained defects has to be considered when the material is exposed to air during plastic deformation at elevated temperatures.

cond-mat.mtrl-sci↗

arXiv2022

Automatic Quantization for Physics-Based Simulation

Quantization has proven effective in high-resolution and large-scale simulations, which benefit from bit-level memory saving. However, identifying a quantization scheme that meets the requirement of both precision and memory efficiency requires trial and error. In this paper, we propose a novel framework to allow users to obtain a quantization scheme by simply specifying either an error bound or a memory compression rate. Based on the error propagation theory, our method takes advantage of auto-diff to estimate the contributions of each quantization operation to the total error. We formulate the task as a constrained optimization problem, which can be efficiently solved with analytical formulas derived for the linearized objective function. Our workflow extends the Taichi compiler and introduces dithering to improve the precision of quantized simulations. We demonstrate the generality and efficiency of our method via several challenging examples of physics-based simulation, which achieves up to 2.5x memory compression without noticeable degradation of visual quality in the results. Our code and data are available at https://github.com/Hanke98/AutoQuantizer.

cs.GR↗

arXiv2022

Elemental (im-)miscibility determines phase formation of multinary nanoparticles co-sputtered in ionic liquids

Non-equilibrium synthesis methods allow to alloy bulk-immiscible elements into multinary nanoparticles, which broadens the design space for new materials. Whereas sputtering onto solid substrates can combine immiscible elements into thin film solid solutions, this is not clear for sputtering of nanoparticles in ionic liquids. Thus, the suitability of sputtering in ionic liquids for producing nanoparticles of immiscible elements is investigated by co-sputtering the systems Au-Cu (miscible), Au-Ru and Cu-Ru (both immiscible), and Au-Cu-Ru on the surface of the ionic liquid 1-butyl-3-methylimidazolium bis-trifluoromethylsulfonyl)imide [Bmim][(Tf)2N]. The sputtered nanoparticles were analyzed to obtain (i) knowledge concerning the general formation process of nanoparticles when sputtering onto ionic liquid surfaces and (ii) information, if alloy nanoparticles of immiscible elements can be synthesized as well as (iii) evidence if the Hume-Rothery rules for solid solubility are valid for sputtered nanoparticles. Accompanying atomistic simulations using density-functional theory for clusters of different size and ordering confirm that the miscibility of Au-Cu and the immiscibility of Au-Ru and Cu-Ru govern the thermodynamic stability of the nanoparticles. Based on the matching experimental and theoretical results for the NP/IL-systems concerning NP stability, a formation model of multinary NPs in ILs was developed.

cond-mat.mtrl-sci↗

arXiv2022

Correlating atom probe tomography with X-Ray and electron spectroscopies to understand microstructure-activity relationships in electrocatalysts

The search for a new energy paradigm with net-zero carbon emissions requires new technologies for energy generation and storage that are at the crossroad between engineering, chemistry, physics, surface and materials sciences. To keep pushing the inherent boundaries of device performance and lifetime, we need to step away from a cook-and-look approach and aim to establish the scientific ground to guide the design of new materials. This requires strong efforts in establishing bridges between microscopy and spectroscopy techniques, across multiple scales. Here, we discuss how the complementarities of X-ray- and electron-based spectroscopies and atom probe tomography can be exploited in the study of surfaces and sub-surfaces to understand structure-property relationships in electrocatalysts.

cond-mat.mtrl-sci↗

arXiv2022

Dynamic doping and Cottrell atmosphere optimize the thermoelectric performance of n-type PbTe

High thermoelectric energy conversion efficiency requires a large figure-of-merit, zT, over a broad temperature range. To achieve this, we optimize the carrier concentrations of n-type PbTe from room up to hot-end temperatures by co-doping Bi and Ag. Bi is an efficient n-type dopant in PbTe, often leading to excessive carrier concentration at room temperature. As revealed by density functional theory calculations, the formation of Bi and Ag defect complexes is exploited to optimize the room temperature carrier concentration. At elevated temperatures, we demonstrate the dynamic dissolution of Ag2Te precipitates in PbTe in situ by heating in a scanning transmission electron microscope. The release of n-type Ag interstitials with increasing temperature fulfills the requirement of higher carrier concentrations at the hot end. Moreover, as characterized by atom probe tomography, Ag atoms aggregate along parallel dislocation arrays to form Cottrell atmospheres. This results in enhanced phonon scattering and leads to a low lattice thermal conductivity. As a result of the synergy of dynamic doping and phonon scattering at decorated dislocations, an average zT of 1.0 is achieved in n-type Bi/Ag-codoped PbTe between 400 and 825 K. Introducing dopants with temperature-dependent solubility and strong interaction with dislocation cores enables simultaneous optimization of the average power factor and thermal conductivity, providing a new concept to exploit in the field of thermoelectrics.

cond-mat.mtrl-sci↗

arXiv2022

Full-color three-loop three-point form factors in N=4 SYM

We present the detailed computation of full-color three-loop three-point form factors of both the stress-tensor supermultiplet and a length-three BPS operator in N=4 SYM. The integrands are constructed based on the color-kinematics (CK) duality and generalized unitarity method. An interesting observation is that the CK-dual integrands contain a large number of free parameters. We discuss the origin of these free parameters in detail and check that they cancel in the simplified integrands. We further perform the numerical evaluation of the integrals at a special kinematics point using public packages FIESTA and pySecDec based on the sector-decomposition approach. We find that the numerical computation can be significantly simplified by expressing the integrals in terms of uniformly transcendental basis, although the final three-loop computations still require large computational resources. Having the full-color numerical results, we verify that the non-planar infrared divergences reproduce the non-dipole structures, which firstly appear at three loops. As for the finite remainder functions, we check that the numerical planar remainder for the stress-tensor supermultiplet is consistent with the known result of the bootstrap computation. We also obtain for the first time the numerical results of the three-loop non-planar remainder for the stress-tensor supermultiplet as well as the three-loop remainder for the length-three operator.

hep-th↗

arXiv2021

Three-loop color-kinematics duality: A 24-dimensional solution space induced by new generalized gauge transformations

We obtain full-color three-loop three-point form factors of the stress-tensor supermultiplet and also of a length-3 half-BPS operator in N=4 SYM based on the color-kinematics duality and on-shell unitarity. The integrand results are verified by all planar and non-planar unitarity cuts, and they satisfy the minimal power-counting of loop momenta and diagrammatic symmetries. Interestingly, these three-loop solutions, while manifesting all dual Jacobi relations, contain a large number of free parameters; in particular, there are 24 free parameters for the form factor of stress-tensor supermultiplet. Such degrees of freedom are due to a new type of generalized gauge transformation associated with the operator insertion for form factors. We also perform numerical integration and obtain consistent full-color infrared divergences and the known planar remainder. The form factors we obtain can be understood as the N=4 SYM counterparts of three-loop Higgs plus three-gluon amplitudes in QCD and are expected to provide the maximally transcendental parts of the latter.

hep-th↗

arXiv2021

Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning

How to select between policies and value functions produced by different training algorithms in offline reinforcement learning (RL) -- which is crucial for hyperpa-rameter tuning -- is an important open question. Existing approaches based on off-policy evaluation (OPE) often require additional function approximation and hence hyperparameters, creating a chicken-and-egg situation. In this paper, we design hyperparameter-free algorithms for policy selection based on BVFT [XJ21], a recent theoretical advance in value-function selection, and demonstrate their effectiveness in discrete-action benchmarks such as Atari. To address performance degradation due to poor critics in continuous-action domains, we further combine BVFT with OPE to get the best of both worlds, and obtain a hyperparameter-tuning method for Q-function based OPE with theoretical guarantees as a side product.

cs.LG↗

arXiv2021

Microphone Array Generalization for Multichannel Narrowband Deep Speech Enhancement

This paper addresses the problem of microphone array generalization for deep-learning-based end-to-end multichannel speech enhancement. We aim to train a unique deep neural network (DNN) potentially performing well on unseen microphone arrays. The microphone array geometry shapes the network's parameters when training on a fixed microphone array, and thus restricts the generalization of the trained network to another microphone array. To resolve this problem, a single network is trained using data recorded by various microphone arrays of different geometries. We design three variants of our recently proposed narrowband network to cope with the agnostic number of microphones. Overall, the goal is to make the network learn the universal information for speech enhancement that is available for any array geometry, rather than learn the one-array-dedicated characteristics. The experiments on both simulated and real room impulse responses (RIR) demonstrate the excellent across-array generalization capability of the proposed networks, in the sense that their performance measures are very close to, or even exceed the network trained with test arrays. Moreover, they notably outperform various beamforming methods and other advanced deep-learning-based methods.

eess.AS↗

arXiv2021

Dopant-segregation to grain boundaries controls electrical conductivity of n-type NbCo(Pt)Sn half-Heusler alloy mediating thermoelectric performance

Science-driven design of future thermoelectric materials requires a deep understanding of the fundamental relationships between microstructure and transport properties. Grain boundaries in polycrystalline materials influence the thermoelectric performance through the scattering of phonons or the trapping of electrons due to space-charge effects. Yet, the current lack of careful investigations on grain boundary-associated features hinders further optimization of properties. Here, we study n-type NbCo1-xPtxSn half-Heusler alloys, which were synthesized by ball milling and spark plasma sintering (SPS). Post-SPS annealing was performed on one sample, leading to improved low-temperature electrical conductivity. The microstructure of both samples was examined by electron microscopy and atom probe tomography. The grain size increases from ~230 nm to ~2.38 μm upon annealing. Pt is found within grains and at grain boundaries, where it locally reduces the resistivity, as assessed by in situ four-point-probe electrical conductivity measurement. Our work showcases the correlation between microstructure and electrical conductivity, providing opportunities for future microstructural optimization by tuning the chemical composition at grain boundaries.

cond-mat.mtrl-sci↗

arXiv2021

Distributed-Memory Tensor Completion for Generalized Loss Functions in Python using New Sparse Tensor Kernels

Tensor computations are increasingly prevalent numerical techniques in data science, but pose unique challenges for high-performance implementation. We provide novel algorithms and systems infrastructure which enable efficient parallel implementation of algorithms for tensor completion with generalized loss functions. Specifically, we consider alternating minimization, coordinate minimization, and a quasi-Newton (generalized Gauss-Newton) method. By extending the Cyclops library, we implement all of these methods in high-level Python syntax. To make possible tensor completion for very sparse tensors, we introduce new multi-tensor primitives, for which we provide specialized parallel implementations. We compare these routines to pairwise contraction of sparse tensors by reduction to hypersparse matrix formats, and find that the multi-tensor routines are more efficient in theoretical cost and execution time in experiments. We provide microbenchmarking results on the Stampede2 supercomputer to demonstrate the efficiency of the new primitives and Cyclops functionality. We then study the performance of the tensor completion methods for a synthetic tensor with 10 billion nonzeros and the Netflix dataset, considering both least squares and Poisson loss functions.

cs.DC↗

arXiv2021

Different Photostability of BiVO4 in Near-pH-Neutral Electrolytes

Photoelectrochemical water splitting is a promising route to produce hydrogen from solar energy. However, corrosion of semiconducting photoelectrodes remains a fundamental challenge for their practical application. The stability of BiVO4, one of the best performing photoanode materials, is systematically examined here using an illuminated scanning flow cell to measure its dissolution operando. The dissolution rates of BiVO4 under illumination depend on the electrolyte and decrease in the order: borate (pH=9.3) > phosphate (pH=7.2) > citrate (pH=7.0). BiVO4 exhibits an inherent lack of stability during the oxygen evolution reaction (OER), while hole-scavenging citrate electrolyte offers kinetic protection. The dissolution of Bi peaks at different potentials than the dissolution of V in phosphate buffer, whereas both ions dissolve simultaneously in borate buffer. The life cycle of a 90 nm BiVO4 film is monitored during one hour of light-driven OER in borate buffer. The photocurrent and dissolution rates show independent trends with time, highlighting the importance to measure both quantities operando. Dissolution rates are correlated to the surface morphology and chemistry characterized using electron microscopy, X-ray photoelectron spectroscopy and atom probe tomography. These correlative measurements further the understanding on corrosion processes of photoelectrodes down to the nanoscopic scale to facilitate their future developments.

cond-mat.mtrl-sci↗

arXiv2020

A feature-supervised generative adversarial network for environmental monitoring during hazy days

The adverse haze weather condition has brought considerable difficulties in vision-based environmental applications. While, until now, most of the existing environmental monitoring studies are under ordinary conditions, and the studies of complex haze weather conditions have been ignored. Thence, this paper proposes a feature-supervised learning network based on generative adversarial networks (GAN) for environmental monitoring during hazy days. Its main idea is to train the model under the supervision of feature maps from the ground truth. Four key technical contributions are made in the paper. First, pairs of hazy and clean images are used as inputs to supervise the encoding process and obtain high-quality feature maps. Second, the basic GAN formulation is modified by introducing perception loss, style loss, and feature regularization loss to generate better results. Third, multi-scale images are applied as the input to enhance the performance of discriminator. Finally, a hazy remote sensing dataset is created for testing our dehazing method and environmental detection. Extensive experimental results show that the proposed method has achieved better performance than current state-of-the-art methods on both synthetic datasets and real-world remote sensing images.

cs.CV↗