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Wang, Han

Publications and source records attributed to Wang, Han.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Deep potential generation scheme and simulation protocol for the Li 10 GeP 2 S 12 -type superionic conductors

We report solid-state electrolyte materials with superior lithium ionic conductivities are vital to the next-generation Li-ion batteries. Molecular dynamics could provide atomic scale information to understand the diffusion process of Li-ion in these superionic conductor materials. Here, we implement the deep potential generator to set up an efficient protocol to automatically generate interatomic potentials for Li 10 GeP 2 S 12 -type solid-state electrolyte materials (Li 10 GeP 2 S 12 , Li 10 SiP 2 S 12 , and Li 10 SnP 2 S 12 ). The reliability and accuracy of the fast interatomic potentials are validated. With the potentials, we extend the simulation of the diffusion process to a wide temperature range (300 K–1000 K) and systems with large size (~1000 atoms). Important technical aspects such as the statistical error and size effect are carefully investigated, and benchmark tests including the effect of density functional, thermal expansion, and configurational disorder are performed. The computed data that consider these factors agree well with the experimental results, and we find that the three structures show different behaviors with respect to configurational disorder. Our work paves the way for further research on computation screening of solid-state electrolyte materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Modeling of flash sintering of ionic ceramics

A fundamental understanding of the influence of defects in ionic ceramics at the atomic, microstructural, and macroscopic levels, before, during, and after the flash sintering event is key to the development of ceramic processing operations that lead to fast, low cost, and environmentally safe fabrication of materials. The observed phenomenology of the flash process encompasses multiple time and length scales and have resulted on a wide variety of what sometimes appears to be contradictory explanations. Here, this article summarizes the latest developments on the modeling and simulation of flash sintering, specifically those related to the understanding of the equilibrium and kinetic properties and the corresponding microstructural evolution of ionic ceramics. Challenges and opportunities in the development of theoretical analyses that include unidentified multiphysical effects are discussed, as they pertain to the processing of technologically relevant ceramic materials for advanced structures and devices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multifunctional Metal–Oxide Nanocomposite Thin Film with Plasmonic Au Nanopillars Embedded in Magnetic La 0.67 Sr 0.33 MnO 3 Matrix

Searching for multifunctional materials with tunable magnetic and optical properties has been a critical task toward the implementation of future integrated optical devices. Vertically aligned nanocomposite (VAN) thin films provide a unique platform for multifunctional material designs. In this work, a new metal–oxide VAN has been designed with plasmonic Au nanopillars embedded in a ferromagnetic La 0.67 Sr 0.33 MnO 3 (LSMO) matrix. Such Au–LSMO nanocomposite presents intriguing plasmon resonance in the visible range and magnetic anisotropy property, which are functionalized by the Au and LSMO phase, respectively. Furthermore, the vertically aligned nanostructure of metal and dielectric oxide results in the hyperbolic property for near-field electromagnetic wave manipulation. Such optical and magnetic response could be further tailored by tuning the composition of Au and LSMO phases.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Porous FeCo Glassy Alloy as Bifunctional Support for High-Performance Zn-Air Battery

Zn-air battery (ZAB) is attracting increasing attention due to its high safety and performance. However, the practical application of ZAB relies heavily on developing durable support materials to replace conventional carbon supports which have unrecoverable corrosion issues, severely jeopardizing ZAB performance. Herein, a novel porous FeCo glassy alloy was developed as bifunctional catalytic support for ZAB. Specifically, the conducting skeleton of the porous glassy alloy was used to stabilize oxygen reduction cocatalysts, and more importantly, the FeCo served as the primary phase for oxygen evolution. To prove the concept of catalytic glassy alloy support, ultra-small Pd nanoparticles were anchored, as oxygen reduction active sites, on the porous FeCo (noted as Pd/FeCo) for ZAB. The Pd/FeCo exhibited a significantly improved electrocatalytic activity for oxygen reduction (a half-wave potential of 0.85 V) and oxygen evolution (a potential of 1.55 V to reach 10 mA cm –2 ) in the alkaline media. When used in the ZAB, the Pd/FeCo delivered an output power density of 117 mW cm –2 and outstanding cycling stability for over 200 h (400 cycles), surpassing the conventional carbon-supported Pt/C+IrO 2 catalysts. Such an integrated design of combining highly active components with porous architecture provides a new strategy to develop novel nanostructured electrocatalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2020

DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory

We propose a general machine learning-based framework for building an accurate and widely applicable energy functional within the framework of generalized Kohn–Sham density functional theory. To this end, we develop a way of training self-consistent models that are capable of taking large datasets from different systems and different kinds of labels. Here, we demonstrate that the functional that results from this training procedure gives chemically accurate predictions on energy, force, dipole, and electron density for a large class of molecules. It can be continuously improved when more and more data are available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Crystal Structure Prediction of Binary Alloys via Deep Potential

Predicting crystal structure has been a challenging problem in physics and materials science for a long time. A reliable energy calculation engine combined with an efficient global search algorithm, such as particle swarm optimization algorithm or genetic algorithm, is needed to conduct crystal structure prediction. In recent years, machine learning-based interatomic potential energy surface models have been proposed, potentially allowing us to perform crystal structure prediction for systems with the accuracy of density functional theory (DFT) and the speed of empirical force fields. In this paper, we employ a previously developed Deep Potential model to predict the intermetallic compound of the aluminum–magnesium system, and find six meta-stable phases with negative or nearly zero formation energy. In particular, Mg 12 Al 8 shows excellent ductility and Mg 5 Al 27 has a high Young's modulus. Based on our benchmark results, we propose a relatively robust structure screening criterion that selects potentially stable structures from the Deep Potential-based convex hull and performs DFT refinement. By using this criterion, the computational cost needed to construct the convex hull with ab initio accuracy can be dramatically reduced.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Beyond Graphene: Low-Symmetry and Anisotropic 2D Materials

Low-symmetry 2D materials—such as ReS 2 and ReSe 2 monolayers, black phosphorus monolayers, group-IV monochalcogenide monolayers, borophene, among others—have more complex atomistic structures than the honeycomb lattices of graphene, hexagonal boron nitride, and transition metal dichalcogenides. The reduced symmetries of these emerging materials give rise to inhomogeneous electron, optical, valley, and spin responses, as well as entirely new properties such as ferroelasticity, ferroelectricity, magnetism, spin-wave phenomena, large nonlinear optical properties, photogalvanic effects, and superconductivity. Novel electronic topological properties, nonlinear elastic properties, and structural phase transformations can also take place due to low symmetry. Lastly, the “Beyond Graphene: Low-Symmetry and Anisotropic 2D Materials” Special Topic was assembled to highlight recent experimental and theoretical research on these emerging materials.

2D materials↗
DOE OSTI2020

Tri-gate GaN junction HEMT

This work presents a tri-gate GaN junction high-electron-mobility transistor (JHEMT) concept in which the p–n junction wraps around the AlGaN/GaN fins in the gate region. This tri-gate JHEMT differs from all existing GaN FinFETs and tri-gate HEMTs, as they employ a Schottky or a metal-insulator-semiconductor (MIS) gate stack. A tri-gate GaN JHEMT is fabricated using p-type NiO with the gate metal forming an Ohmic contact to NiO. The device shows minimal hysteresis and a subthreshold slope of 63 ± 2 mV/decade with an on-off current ratio of 10 8 . Compared to the tri-gate MISHEMTs fabricated on the same wafer, the tri-gate JHEMTs exhibit higher threshold voltage (V TH ) and achieve positive V TH without the need for additional AlGaN recess. In addition, this tri-gate JHEMT with a fin width of 60 nm achieves a breakdown voltage (BV) > 1500 V (defined at the drain current of 1 μA/mm at zero gate bias) and maintains the high BV with the fin length scaled down to 200 nm. In comparison, the tri-gate MISHEMTs with narrower and longer fins show punch-through at high voltages. Moreover, when compared to planar enhancement mode HEMTs, tri-gate JHEMTs show significantly lower channel sheet resistance in the gate region. Finally, these results illustrate a stronger channel depletion and electrostatic control in the junction tri-gate compared to the MIS tri-gate and suggest great promise of the tri-gate GaN JHEMTs for both high-voltage power and low-voltage power/digital applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Revealing electronic state-switching at conical intersections in alkyl iodides by ultrafast XUV transient absorption spectroscopy

Conical intersections between electronic states often dictate the chemistry of photoexcited molecules. Recently developed sources of ultrashort extreme ultraviolet (XUV) pulses tuned to element-specific transitions in molecules allow for the unambiguous detection of electronic state-switching at a conical intersection. Here, the fragmentation of photoexcited iso-propyl iodide and tert-butyl iodide molecules (i-C 3 H 7 I and t-C 4 H 9 I) through a conical intersection between 3 Q 0 / 1 Q 1 spin-orbit states is revealed by ultrafast XUV transient absorption measuring iodine 4d core-to-valence transitions. The electronic state-sensitivity of the technique allows for a complete mapping of molecular dissociation from photoexcitation to photoproducts. In both molecules, the sub-100 fs transfer of a photoexcited wave packet from the 3 Q 0 state into the 1 Q 1 state at the conical intersection is captured. The results show how differences in the electronic state-switching of the wave packet in i-C 3 H 7 I and t-C 4 H 9 I directly lead to differences in the photoproduct branching ratio of the two systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Ground State Energy Functional with Hartree–Fock Efficiency and Chemical Accuracy

We introduce the deep post Hartree–Fock (DeePHF) method, a machine learning-based scheme for constructing accurate and transferable models for the ground-state energy of electronic structure problems. DeePHF predicts the energy difference between results of highly accurate models such as the coupled cluster method and low accuracy models such as the Hartree–Fock (HF) method, using the ground-state electronic orbitals as the input. It preserves all the symmetries of the original high accuracy model. The added computational cost is less than that of the reference HF or DFT and scales linearly with respect to system size. We examine the performance of DeePHF on organic molecular systems using publicly available data sets and obtain the state-of-art performance, particularly on large data sets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Deep neural network for the dielectric response of insulators

In this work, we introduce a deep neural network to model in a symmetry preserving way the environmental dependence of the centers of the electronic charge. The model learns from ab initio density functional theory, wherein the electronic centers are uniquely assigned by the maximally localized Wannier functions. When combined with the deep potential model of the atomic potential energy surface, the scheme predicts the dielectric response of insulators for trajectories inaccessible to direct ab initio simulation. The scheme is nonperturbative and can capture the response of a mutating chemical environment. We demonstrate the approach by calculating the infrared spectra of liquid water at standard conditions, and of ice under extreme pressure, when it transforms from a molecular to an ionic crystal.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗