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Yang, Chao

Publications and source records attributed to Yang, Chao.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

ELSI — An open infrastructure for electronic structure solvers

Routine applications of electronic structure theory to molecules and periodic systems need to compute the electron density from given Hamiltonian and, in case of non-orthogonal basis sets, overlap matrices. System sizes can range from few to thousands or, in some examples, millions of atoms. Different discretization schemes (basis sets) and different system geometries (finite non-periodic vs. infinite periodic boundary conditions) yield matrices with different structures. The ELectronic Structure Infrastructure (ELSI) project provides an open-source software interface to facilitate the implementation and optimal use of high-performance solver libraries covering cubic scaling eigensolvers, linear scaling density-matrix-based algorithms, and other reduced scaling methods in between. In this paper, we present recent improvements and developments inside ELSI, mainly covering (1) new solvers connected to the interface, (2) matrix layout and communication adapted for parallel calculations of periodic and/or spin-polarized systems, (3) routines for density matrix extrapolation in geometry optimization and molecular dynamics calculations, and (4) general utilities such as parallel matrix I/O and JSON output. The ELSI interface has been integrated into four electronic structure code projects (DFTB+, DGDFT, FHI-aims, SIESTA), allowing us to rigorously benchmark the performance of the solvers on an equal footing. Based on results of a systematic set of large-scale benchmarks performed with Kohn–Sham density-functional theory and density-functional tight-binding theory, we identify factors that strongly affect the efficiency of the solvers, and propose a decision layer that assists with the solver selection process. As a result, we describe a reverse communication interface encoding matrix-free iterative solver strategies that are amenable, e.g., for use with planewave basis sets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Stable diagonal stripes in the t-J model at $\bar{n}$ h = 1/8 doping from fPEPS calculations

We investigate the two-dimensional t–J model at a hole doping of $\bar{n}$ h= 1 / 8 using recently developed high accuracy fermionic projected entangled pair states method. By applying the stochastic gradient descent method combined with the Monte Carlo sampling technique, we obtain the ground state hole energy Ehole = -1.621 for J/t = 0.4. We show that the ground state has stable diagonal stripes instead of vertical stripes with a width of 4 unit cells, and stripe filling ρ l = 0.5. We further show that the long-range superconductivity order is suppressed at this point.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Scalable implementation of polynomial filtering for density functional theory calculation in PARSEC

In this work, we present an efficient implementation of polynomial filtering methods in PARSEC, a real-space pseudopotential based Kohn–Sham density functional theory solver. The implementation described here improves upon a Chebyshev-filtered subspace iteration algorithm used in the previous version of PARSEC. We present a hybrid polynomial filtering scheme that combines Chebyshev-filtered subspace iteration and a spectrum slicing method that partitions the spectrum into several spectral slices and uses bandpass-filtered subspace iteration to compute approximate eigenpairs within each interior slice simultaneously. We describe a procedure to partition a spectrum and construct polynomial filters. We also discuss a number of practical issues such as the use of appropriate data layouts for carrying out the computation on a two-dimensional process grid and how to achieve good load balance by allocating an appropriate number of process groups to each spectral slice. Numerical examples are presented to demonstrate the effectiveness of the hybrid polynomial filtering method as well as the superior parallel scalability of spectrum slicing in comparison to that of Chebyshev-filtered subspace iteration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Anomalously Suppressed Thermal Conduction by Electron-Phonon Coupling in Charge-Density-Wave Tantalum Disulfide

Charge and thermal transport in a crystal is carried by free electrons and phonons (quantized lattice vibration), the two most fundamental quasiparticles. Above the Debye temperature of the crystal, phonon-mediated thermal conductivity ( κ L ) is typically limited by mutual scattering of phonons, which results in κ L decreasing with inverse temperature, whereas free electrons play a negligible role in κ L . Here, an unusual case in charge-density-wave tantalum disulfide (1T-TaS 2 ) is reported, in which κ L is limited instead by phonon scattering with free electrons, resulting in a temperature-independent κ L . In this system, the conventional phonon–phonon scattering is alleviated by its uniquely structured phonon dispersions, while unusually strong electron-phonon (e-ph) coupling arises from its Fermi surface strongly nested at wavevectors in which phonons exhibit Kohn anomalies. The unusual temperature dependence of thermal conduction is found as a consequence of these effects. The finding reveals new physics of thermal conduction, offers a unique platform to probe e-ph interactions, and provides potential ways to control heat flow in materials with free charge carriers. The temperature-independent thermal conductivity may also find thermal management application as a special thermal interface material between two systems when the heat conduction between them needs to be maintained at a constant level.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Acousto-optic mode-locked Tm:LuAG laser with nearly diffraction-limited beam

We reported an acousto-optic mode-locked Tm:LuAG laser pumped by a 786 nm laser diode and realized continuous-wave mode-locked operation. At the pump power of 8.2 W, the output average power was 335 mW with the wavelength of 2023.2 nm. The pulse width was 240 ps at the pulse repetition frequency of 81.60 MHz, and the beam quality factor M{sup 2} was 1.04. The output wavelength was tuned from 2022.6 to 2025.3 nm, corresponding to the output average power of 310 to 353 mW. In addition, the self-mode-locking phenomenon was found.

42 ENGINEERING↗
DOE OSTI2019

Enhanced mechanical properties of carbon-doped FeNiMnAlCr high entropy alloy via hot-rolling

This work demonstrates an improvement in the mechanical performance of the high entropy alloy Fe 40.4 Ni 11.3 Mn 34.8 Al 7.5 Cr 6 containing 1.1 at. % carbon (CHEA) by hot-rolling followed by annealing at 1323 K. The microstructure was examined using scanning electron microscopy, electron backscatter diffraction, X-ray diffraction, and transmission electron microscopy. Room-temperature tensile tests were used to assess the alloy's mechanical properties. The presence of dense dislocation walls indicate that the alloy has not fully recrystallized and, moreover, the deformation substructure and the large fraction of Σ3 twin boundaries contribute to the high room temperature yield strength (~523 MPa). Despite the presence of substantial dislocation density, the annealed, hot-rolled CHEA displays an improvement in elongation to fracture (~33%) compared to the negligible ductility (< 2%) observed for the alloy after it has been simply annealed. Finally, the present work shows that hot rolling can produce good strength and ductility in the alloy while establishing the processing conditions necessary for the large-scale manufacture and utilization of its remarkable properties.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Stationary Spatial Charging Demand Distribution for Commercial Electric Vehicles in Urban Area

The electric vehicle (EV) is an increasingly popu- lar mobility solution today. It is especially appealing for urban commercial usage including serving commuters and delivering goods. Commercial EVs, defined as electric taxi fleets in this paper, contribute a lot to the energy consumption and emission. One critical component to facilitate the adoption of EVs is to provide sufficient charging infrastructures for road users to alleviate the range anxiety. In this study, we develop an analytical model to profile the stationary spatial distribution of charging demand for any given fleet of commercial EVs, which serves as the necessary input for planning the location of charging infrastructures. Our model considers the movement of EVs as a random walk, with the underlying Markov process driven by the spatial demand that the commercial EV fleet needs to serve. We then define the electricity consumption and charging models and develop the probability distribution that an EV may need to recharge its battery at any given location in the city, based on the stationary distribution of the random walk process. We calibrate the performance parameters of EVs from the trip record and present numerical experiments based on New York City taxi trip data and compare the solution from simulation with our analytical solution. The result shows that analytical solution is quite close to the simulation result. This paper provides insights to the city agencies and private companies to make charging infrastructure location planning.

Qian, Xinwu↗
DOE OSTI2019

Pressure effect on Kohn anomaly and electronic topological transition in single-crystal tantalum

The Kohn anomaly and topological change of the Fermi surface in d -block metals can occur under high pressure with affiliated significant changes in elastic, mechanical, and transport properties. However, our understanding of their origin and associated physical phenomena remains limited both experimentally and theoretically. In this paper, we study the pressure effect on the Kohn anomaly, electronic topological transition (ETT), and the associated anomalies in physical properties of body-centered cubic (bcc) single-crystal tantalum (Ta). The phonon dispersions of Ta crystal were directly measured up to ~47 GPa using high-energy resolution inelastic x-ray scattering in a diamond anvil cell with hydrostatic helium medium. A Kohn anomaly in Ta was observed and became significantly stronger at 47.0 GPa at the reduced wave vector of ~0.7 in the longitudinal acoustic mode along the [ξ ,0,0] direction. Our theoretical and experimental results indicate that the electron-phonon coupling and Fermi surface nesting mainly contribute to the Kohn anomaly, and the latter plays a dominant role at high pressures of 17–47 GPa. First-principles calculations further reveal an ETT with a topology change of the Fermi surface to occur at ~100 GPa in Ta, which causes a softening in the elastic constants ( C 11 and C 44 ) and mechanical properties (shear, Young’s, and bulk moduli). Our study shows that the d-orbital electrons in Ta play a key role in the stability of its electronic topological structure, where electron doping in Ta could significantly depress its ETT and elastic anomaly at high pressures. Finally, it is conceivable that our observed Kohn anomaly and ETT in a representative bcc Ta are much more prevalent in d-block transition metals under compression than previously thought.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A Shift Selection Strategy for Parallel Shift-Invert Spectrum Slicing in Symmetric Self-Consistent Eigenvalue Computation

The central importance of large scale eigenvalue problems in scientific computation necessitates the development massively parallel algorithms for their solution. Recent advances in dense numerical linear algebra have enabled the routine treatment of eigenvalue problems with dimensions on the order of hundreds of thousands on the world's largest supercomputers. In cases where dense treatments are not feasible, Krylov subspace methods offer an attractive alternative due to the fact that they do not require storage of the problem matrices. However, demonstration of scalability of either of these classes of eigenvalue algorithms on computing architectures capable of expressing excessive parallelism is non-trivial due to communication requirements and serial bottlenecks, respectively. In this work, we introduce the SISLICE method: a parallel shift-invert algorithm for the solution of the symmetric self-consistent field (SCF) eigenvalue problem. The SISLICE method drastically reduces the communication requirement of current parallel shift-invert eigenvalue algorithms through various shift selection and migration techniques based on density of states estimation and k-means clustering, respectively. This work demonstrates the robustness and parallel performance of the SISLICE method on a representative set of SCF eigenvalue problems and outlines research directions which will be explored in future work.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Surgical Resection for Metastatic Tumors in the Pancreas: A Single-Center Experience and Systematic Review

Background: Metastatic lesion to the pancreas accounts for approximately 2% of pancreatic neoplasms. There is no prospective, randomized or case-controlled study evaluating the role of pancreatic metastasectomy. Methods: The PubMed, EMBASE, and Cochrane Library electronic databases were searched for studies published between January 1, 2001 and December 31, 2017. Studies with five or more patients who received pancreatic metastasectomy and data from our institution (29 patients) were included. The Kaplan–Meier method was used for survival analysis. Results: A total of 414 patients from 20 institutions who underwent pancreatic resections were included. Of the reported 31 kinds of primary neoplasms, renal-cell carcinoma (RCC) comprised the most (54.3%). At the time of diagnosis, although 40.3% patients were asymptomatic, abdominal pain (34.8%) and jaundice (20.6%) were relatively common. As for surgical type, pancreatoduodenectomy, total pancreatectomy, distal pancreatectomy, and enucleation took up 37.9%, 11.4%, 43.5%, and 7.2% respectively. The mortality and morbidity rates were 1.4% and 48.3% respectively. Patients with symptoms at the time of diagnosis had significantly shorter survival compared with asymptomatic patients (p = 0.017). Those with RCC as primary tumor had significantly longer survival compared with non-RCC patients (p < 0.001). Positive margin also predicts worse prognosis (p = 0.035). Conclusions: Pancreatic metastasectomy is safe and associated with acceptable short- and intermediate-term results. In the conditions of RCC as the primary tumor, being asymptomatic, or negative resection margin, a better prognosis after resection can be achieved.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Low-temperature anharmonicity and the thermal conductivity of cesium iodide

Cesium halide has a simple cubic crystal structure and hosts low thermal conductivity, but its microscopic mechanism has not been fully understood. Here, we took cesium iodide (CsI) single crystal as an example, to investigate the lattice dynamics and thermal conductivity by performing inelastic neutron scattering (INS), heat transport measurements, and first-principles calculations. The temperature dependent phonon dispersions of CsI were obtained from INS and the low temperature anharmonicity of transverse optic (o) and transverse acoustic (a) phonon modes in CsI was observed. By performing the thermal conductivity measurement and first-principles calculations, it is shown that the low thermal conductivity of CsI originates from the combined effect of the small phonon group velocities and the large phonon scattering rates, which is dominated by the (a,a,a) and (a,a,o) phonon scattering processes. Lastly, this work highlights the importance of phonon anharmonicity in lattice dynamics, which sheds light on the design of materials with low thermal conductivity.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Approximate Green’s Function Coupled Cluster A Method Employing Effective Dimension Reduction

The Green’s function coupled cluster (GFCC) method, originally proposed in the early 1990s, is a powerful many-body tool for computing and analyzing the electronic structures of molecular structures and periodic systems, especially when electrons are strongly correlated. However, in order for the GFCC method to become an approach that can be routinely used in the electronic structure calculations, more robust linear algebraic techniques and approximations need to be employed to reduce its extremely high computational overhead. In our recent studies, we showed that the GFCC equation can be numerically solved by seeking solutions of multiple linear systems in the frequency regime of interest, which can be easily distributed in a massively parallel environment, and broaden the avenue for more state-of-the-art numerical linear algebraic tools associated with multiple linear systems to be kicked in to this field. In the present pursuit of this practice, we show a successful application of model order reduction (MOR) techniques in the GFCC framework. Briefly speaking, for a frequency regime of interest that requires a high resolution of spectral function, instead of solving GFCC linear equation in the original dimension for every single frequency point, an easily-solvable linear system model with reduced dimension can be built upon projecting the original GFCC linear system into a subspace, from which the interpolation and extrapolation can give a reasonable approximation to the results of the original GFCC linear equations. Here, we show that the subspace can be properly constructed in an iterative manner from the auxiliary vectors of the GFCC linear equations at some select frequencies inside the frequency regime. During the iterations, the quality of the subspace, as well as the linear system model, can be systematically improved. The approach is tested in this work in terms of the efficiency and accuracy of computing spectral functions for some typical molecular systems including carbon monoxide, 1,3-butadiene, benzene, and adenine molecules. As a byproduct, the high quality linear system model is also found to be able to provide high quality initial guess for the existing linear solver to improve the convergence performance.

Peng, Bo↗
DOE OSTI2019

Solving a trillion unknowns per second with HPGMG on Sunway TaihuLight

Benchmarks for supercomputers are important tools, not only for evaluating and ranking modern supercomputers, but also for providing hints for future architecture design. As a new benchmark, HPGMG (high performance geometric multigrid) solves a linear equation set with a full geometric multi-grid algorithm. It involves computation on different scales, data movement with various volumes, global communication and neighbor communication with both large and small messages, etc., and is more correlated to real world applications than traditional benchmarks such as LINPACK. Therefore, it is desirable to examine how well HPGMG can perform on leadership supercomputers such as Sunway Taihulight. Sunway Taihulight, the No. 1 supercomputer in the Top 500 list from June 2016 to June 2018, which uses a specially designed many-core architecture SW26010, is of great interest to the community of high performance computing. With careful analysis and code design, we came up with an efficient implementation of HPGMG on SW26010 processors. We not only employed traditional optimization techniques such as 2.5D partitioning, double buffering, and collective data load, but also introduced a micro-benchmark to help with the choice of optimization direction and parameter tuning. Another contribution is that we proposed a new procedure for the major operations, by granulating and reordering the smooth function and the ghost exchange operation, leading to reduced memory copy and accelerated communication process. Our optimized implementation of HPGMG on Sunway TaihuLight achieved a ground-breaking performance of 1.036 × 10 12 Degrees of Freedom per second at the finest level, which is No. 1 on the HPGMG list of Nov 2017.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Deep learning: Extrapolation tool for ab initio nuclear theory

Ab initio approaches in nuclear theory, such as the no-core shell model (NCSM), have been developed for approximately solving finite nuclei with realistic strong interactions. The NCSM and other approaches require an extrapolation of the results obtained in a finite basis space to the infinite basis space limit and assessment of the uncertainty of those extrapolations. Each observable requires a separate extrapolation and many observables have no proven extrapolation method. Herein, we propose a feed-forward artificial neural network (ANN) method as an extrapolation tool to obtain the ground-state energy and the ground-state point-proton root-mean-square (rms) radius along with their extrapolation uncertainties. The designed ANNs are sufficient to produce results for these two very different observables in 6 Li from the ab initio NCSM results in small basis spaces that satisfy the following theoretical physics condition: independence of basis space parameters in the limit of extremely large matrices. Comparisons of the ANN findings with other extrapolation methods are also provided.

6Li↗
DOE OSTI2019

Data-Driven Multi-Step Demand Prediction for Ride-Hailing Services Using Convolutional Neural Network

Ride-hailing services are growing rapidly and becoming one of the most disruptive technologies in the transportation realm. Accurate prediction of ride-hailing trip demand not only enables cities to better understand people's activity patterns, but also helps ride-hailing companies and drivers make informed decisions to reduce deadheading vehicle miles traveled, traffic congestion, and energy consumption. In this study, a convolutional neural network (CNN)-based deep learning model is proposed for multi-step ride-hailing demand prediction using the trip request data in Chengdu, China, offered by DiDi Chuxing. The CNN model is capable of accurately predicting the ride-hailing pick-up demand at each 1-km by 1-km zone in the city of Chengdu for every 10 min. Compared with another deep learning model based on long short-term memory, the CNN model is 30% faster for the training and predicting process. The proposed model can also be easily extended to make multi-step predictions, which would benefit the on-demand shared autonomous vehicles applications and fleet operators in terms of supply-demand rebalancing. The prediction error attenuation analysis shows that the accuracy stays acceptable as the model predicts more steps.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Concurrent coupling of atomistic simulation and mesoscopic hydrodynamics for flows over soft multi-functional surfaces

We develop an e?cient parallel multiscale method that bridges the atomistic and mesoscale regimes, from nanometers to microns and beyond, via concurrent coupling of atomistic simulation and mesoscopic dynamics. In particular, we combine an all-atom molecular dynamics (MD) description for specific atomistic details in the vicinity of the functional surface with a dissipative particle dynamics (DPD) approach that captures mesoscopic hydrodynamics in the domain away from the functional surface. In order to achieve a seamless transition in dynamic properties we endow the MD simulation with a DPD thermostat, which is validated against experimental results by modeling water at di?erent temperatures. We then validate the MD-DPD coupling method for transient Couette and Poiseuille flows, demonstrating that the concurrent MD-DPD coupling can resolve accurately the continuumbased analytical solutions. Subsequently, we simulate shear flows over grafted polydimethylsiloxane (PDMS) surfaces (polymer brushes) for various grafting densities, and investigate the slip flow as a function of the shear stress. We verify that a ‘‘universal’’ power law exists for the slip length, in agreement with published results. Having validated the MD-DPD coupling method, we simulate timedependent flows past an endothelial glycocalyx layer (EGL) in a microchannel. Coupled simulation results elucidate the dynamics of the EGL changing from an equilibrium state to a compressed state under shear by aligning the molecular structures along the shear direction. MD-DPD simulation results agree well with results of a single MD simulation, but with the former more than two orders of magnitude faster than the latter for system sizes above one micron.

Wang, Yuying↗