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Ong, Shyue Ping

Publications and source records attributed to Ong, Shyue Ping.

35 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Quantum materials for energy-efficient neuromorphic computing: Opportunities and challenges

Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short- and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This Perspective discusses select examples of these approaches and provides an outlook on the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Universal Machine Learning Model for Elemental Grain Boundary Energies

The grain boundary (GB) energy has a profound influence on the grain growth and properties of polycrystalline metals. Here, we show that the energy of a GB, normalized by the bulk cohesive energy, can be described purely by four geometric features. By machine learning on a large computed database of 361 small Σ (Σ<10) GBs of more than 50 metals, we develop a model that can predict the grain boundary energies to within a mean absolute error of 0.13 J m –2 . More importantly, this universal GB energy model can be extrapolated to the energies of high Σ GBs without loss in accuracy. These results highlight the importance of capturing fundamental scaling physics and domain knowledge in the design of interpretable, extrapolatable machine learning models for materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Electrochemically induced amorphous-to-rock-salt phase transformation in niobium oxide electrode for Li-ion batteries

Intercalation-type metal oxides are promising negative electrode materials for safe rechargeable lithium-ion batteries due to the reduced risk of Li plating at low voltages. Nevertheless, their lower energy and power density along with cycling instability remain bottlenecks for their implementation, especially for fast-charging applications. Here, we report a nanostructured rock-salt Nb 2 O 5 electrode formed through an amorphous-to-crystalline transformation during repeated electrochemical cycling with Li + . This electrode can reversibly cycle three lithiums per Nb 2 O 5 , corresponding to a capacity of 269 mAh g –1 at 20 mA g –1 , and retains a capacity of 191 mAh g –1 at a high rate of 1 A g –1 . Further, it exhibits superb cycling stability with a capacity of 225 mAh g –1 at 200 mA g –1 for 400 cycles, and a Coulombic efficiency of 99.93%. We attribute the enhanced performance to the cubic rock-salt framework, which promotes low-energy migration paths. Our work suggests that inducing crystallization of amorphous nanomaterials through electrochemical cycling is a promising avenue for creating unconventional high-performance metal oxide electrode materials.

25 ENERGY STORAGE↗
DOE OSTI2022

Recent advances and applications of deep learning methods in materials science

Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Elucidating the Determinants of Alkali Ionic Conductivity in Oxide and Sulfide Frameworks

The aim of this project is to elucidate the structural and chemical factors determining alkali conductivity in oxide and sulfide frameworks using large scale first principles calculations and topological analysis. The facile conduction of alkali ions in oxide and sulfide host structures is of critical importance in energy storage. Today, the dominant form of energy storage in portable electronics is the rechargeable alkali-ion battery, a device that functions entirely on the basis of the reversible transport of alkali ions. The Li+/Na+ conductivity of a cathode has a direct influence on the rate capability of a Li/Na-ion battery, i.e., the speed at which it can be charged and discharged. Alkali conductivity is arguably of even greater importance in the solid electrolytes currently being investigated for high safety, high energy all-solid-state batteries. The research performed in this project has provided critical new insights into the diffusion mechanisms in state-of-the-art alkali superionic conductors such as Li 7 P 3 S 11 , Li 3 OCl 1-x Br x anti-perovskite, argyrodite Li 6 PS 5 Cl. Two potential novel lithium superionic conductors (Li 3 Y(PS 4 ) 2 and Li 5 PS 4 Cl 2 ) with improved electrochemical stabilities were predicted. We have also conducted in-depth studies into alkali conduction in several important classes of electrodes, including the layered P2 NaMO 2 oxides and the highly promising novel disordered rock salt Li 3+x V 2 O 5 anode for high-rate applications. This project has resulted in more than 10 peer-reviewed articles in highly regarded journals as well as a new open-source software framework (pymatgen-analysis-diffusion) that is widely used by the materials research community for the study of diffusion in materials.

25 ENERGY STORAGE↗
DOE OSTI2021

Lithium and sodium superionic conductors

Presented are new, earth-abundant lithium superionic conductors, Li3Y(PS4)2 and L15PS4Cl2, that emerged from a comprehensive screening of the Li—P—S and Li—M—P—S chemical spaces. Both candidates are derived from the relatively unexplored quaternary silver thiophosphates. One key enabler of this discovery is the development of a first-of-its-kind high-throughput first principles screening approach that can exclude candidates unlikely to satisfy the stringent Li+ conductivity requirements using a minimum of computational resources. Both candidates are predicted to be synthesizable, and are electronically insulating. Systems and methods according to present principles enable new, all-solid-state rechargeable lithium-ion batteries.

Zhu, Zhuoying↗
DOE OSTI2021

Proton distribution visualization in perovskite nickelate devices utilizing nanofocused x rays

We use a 30-nm x-ray beam to study the spatially resolved properties of a SmNiO 3 -based nanodevice that is doped with protons. The x-ray absorption spectra supported by density-functional theory simulations show partial reduction of nickel valence in the region with high proton concentration, which leads to the insulating behavior. Concurrently, x-ray diffraction reveals only a small lattice distortion in the doped regions. Together, our results directly show that the knob which proton doping modifies is the electronic valency and not the crystal lattice. Overall, the studies are relevant to ongoing efforts to disentangle structural and electronic effects across metal-insulator phase transitions in correlated oxides.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Inherent stochasticity during insulator–metal transition in VO 2

Vanadium dioxide (VO 2 ), which exhibits a near-room-temperature insulator–metal transition, has great potential in applications of neuromorphic computing devices. Although its volatile switching property, which could emulate neuron spiking, has been studied widely, nanoscale studies of the structural stochasticity across the phase transition are still lacking. In this study, using in situ transmission electron microscopy and ex situ resistive switching measurement, we successfully characterized the structural phase transition between monoclinic and rutile VO 2 at local areas in planar VO 2 /TiO 2 device configuration under external biasing. After each resistive switching, different VO 2 monoclinic crystal orientations are observed, forming different equilibrium states. We have evaluated a statistical cycle-to-cycle variation, demonstrated a stochastic nature of the volatile resistive switching, and presented an approach to study in-plane structural anisotropy. Furthermore, our microscopic studies move a big step forward toward understanding the volatile switching mechanisms and the related applications of VO 2 as the key material of neuromorphic computing.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A framework for quantifying uncertainty in DFT energy corrections

In this work, we demonstrate a method to quantify uncertainty in corrections to density functional theory (DFT) energies based on empirical results. Such corrections are commonly used to improve the accuracy of computational enthalpies of formation, phase stability predictions, and other energy-derived properties, for example. We incorporate this method into a new DFT energy correction scheme comprising a mixture of oxidation-state and composition-dependent corrections and show that many chemical systems contain unstable polymorphs that may actually be predicted stable when uncertainty is taken into account. We then illustrate how these uncertainties can be used to estimate the probability that a compound is stable on a compositional phase diagram, thus enabling better-informed assessments of compound stability.

42 ENGINEERING↗
DOE OSTI2021

A stable cathode-solid electrolyte composite for high-voltage, long-cycle-life solid-state sodium-ion batteries

Rechargeable solid-state sodium-ion batteries (SSSBs) hold great promise for safer and more energy-dense energy storage. However, the poor electrochemical stability between current sulfide-based solid electrolytes and high-voltage oxide cathodes has limited their long-term cycling performance and practicality. Here, we report the discovery of the ion conductor Na 3-x Y 1-x Zr x Cl 6 (NYZC) that is both electrochemically stable (up to 3.8 V vs. Na/Na + ) and chemically compatible with oxide cathodes. Its high ionic conductivity of 6.6 × 10 –5 S cm –1 at ambient temperature, several orders of magnitude higher than oxide coatings, is attributed to abundant Na vacancies and cooperative MCl 6 rotation, resulting in an extremely low interfacial impedance. A SSSB comprising a NaCrO 2 + NYZC composite cathode, Na 3 PS 4 electrolyte, and Na-Sn anode exhibits an exceptional first-cycle Coulombic efficiency of 97.1% at room emperature and can cycle over 1000 cycles with 89.3% capacity retention at 40 °C. These findings highlight the immense potential of halides for SSSB applications.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Metal-insulator transition in V 2 O 3 with intrinsic defects

Vanadium sesquioxide (V 2 O 3 ) is a Mott insulator exhibiting a temperature-dependent metal-insulator transition (MIT) at 165 K accompanied by both a magnetic and structural transition. Although it is expected to be a metal under conventional band theory, electron interactions at low temperature cause it to behave like an insulator, making it difficult to accurately model its electronic properties with standard ab initio methods. As such, accurate theoretical assessments of the MIT with point defects requires special attention to the type of functionals used. In this study, we conclude that the PBE + U functional provides the best compromise between accuracy and efficiency in calculating the properties related to the MIT between low-temperature and high-temperature V 2 O 3 . We use this functional to explore the various influences that intrinsic point defects will have on the MIT in V 2 O 3 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Tunable Lithium-Ion Transport in Mixed-Halide Argyrodites Li 6–x PS 5–x ClBr x : An Unusual Compositional Space

Argyrodites, with fast lithium-ion conduction, are promising for applications in rechargeable solid-state lithium-ion batteries. In this article, we report a new compositional space of argyrodite superionic conductors, Li 6– x PS 5– x ClBr x [0 ≤ x ≤ 0.8], with a remarkably high ionic conductivity of 24 mS/cm at 25 °C for Li 5.3 PS 4.3 ClBr 0.7 . In addition, the extremely low lithium migration barrier of 0.155 eV makes Li 5.3 PS 4.3 ClBr 0.7 highly promising for low-temperature operation. Average and local structure analyses reveal that bromination ( x > 0) leads to (i) retention of the parent Li 6 PS 5 Cl structure for a wide range of x in Li 6– x PS 5– x ClBr x (0 ≤ x ≤ 0.7), (ii) co-occupancy of Cl – , Br – , and S 2– at 4a/4d sites, and (iii) gradually increased Li + -ion dynamics, eventually yielding a “liquid-like” Li-sublattice with a flattened energy landscape when x approaches 0.7. In addition, the diversity of anion species and Li-deficiency in halogen-rich Li 6– x PS 5– x ClBr x induce hypercoordination and coordination entropy for the Li-sublattice, also leading to enhanced Li + -ion transport in Li 6– x PS 5– x ClBr x . This study demonstrates that mixed-anion framework can help stabilize highly conductive structures in a compositional space otherwise unstable with lower anion diversity.

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

Multiprincipal Component P2-Na 0.6 (Ti 0.2 Mn 0.2 Co 0.2 Ni 0.2 Ru 0.2 )O 2 as a High-Rate Cathode for Sodium-Ion Batteries

Mixing transition metal cations in nearly equiatomic proportions in layered oxide cathode materials is a new strategy for improving the performances of Na-ion batteries. The mixing of cations not only offers entropic stabilization of the crystal structure but also benefits the diffusion of Na ions with tuned diffusion activation energy barriers. In light of this strategy, a high-rate Na 0.6 (Ti 0.2 Mn 0.2 Co 0.2 Ni 0.2 Ru 0.2 )O 2 cathode was designed, synthesized, and investigated, combining graph-based deep learning calculations and complementary experimental characterizations. This new cathode material delivers high discharge capacities of 164 mA g –1 at 0.1 C and 68 mAh g –1 at a very high rate of 86 C, demonstrating an outstanding high rate capability. Ex situ and operando synchrotron X-ray diffraction were used to reveal the detailed structural evolution of the cathode upon cycling. Using the climbing-image nudged elastic-band calculation and Ab initio molecular dynamics simulations, we show that the optimal transition metal composition enables a percolating network of low barrier pathways for fast, macroscopic Na diffusion, resulting in the observed high rate performance.

25 ENERGY STORAGE↗
DOE OSTI2020

Vanadyl Phosphates A x VOPO 4 (A = Li, Na, K) as Multielectron Cathodes for Alkali-Ion Batteries

Vanadyl phosphates comprise a class of multielectron cathode materials capable of cycling two Li + , about 1.66 Na + , and some K + ions per redox center. In this review, structures, thermodynamic stabilities, and ion diffusion kinetics of various A x VOPO 4 (A = Li, Na, K, NH 4 ) polymorphs are discussed. Both the experimental data and first-principle calculations indicate kinetic limitations for alkali metal ions cycling, especially between for 0 ≤ x ≤ 1, and metastability of phases with x > 1. This creates challenges for multiple-ion cycling, as the slow kinetics call for nanosized particles, which being metastable and reactive with organic electrolytes are prone to side reactions. Thus, various synthesis approaches, surface coating, and transition metal ion substitution strategies are discussed here as possible ways to stabilize A x VOPO 4 structures and improve alkali metal ion diffusion. Finally, the role of advanced characterization techniques, such as X-ray absorption spectroscopy, diffraction, pair distribution function analysis and 7 Li and 31 P NMR, in understanding the reaction mechanism from both structural and electronic points of view is emphasized.

25 ENERGY STORAGE↗
DOE OSTI2020

Rechargeable Alkali-Ion Battery Materials: Theory and Computation

Since its development in the 1970s, the rechargeable alkali-ion battery has proven to be a truly transformative technology, providing portable energy storage for devices ranging from small portable electronics to sizable electric vehicles. Here, we present a review of modern theoretical and computational approaches to the study and design of rechargeable alkali-ion battery materials. Additionally, starting from fundamental thermodynamics and kinetics phenomenological equations, we rigorously derive the theoretical relationships for key battery properties, such as voltage, capacity, alkali diffusivity, and other electrochemically relevant computable quantities. We then present an overview of computational techniques for the study of rechargeable alkali-ion battery materials, followed by a critical review of the literature applying these techniques to yield crucial insights into battery operation and performance. Finally, we provide perspectives on outstanding challenges and opportunities in the theory and computation of rechargeable alkali-ion battery materials.

25 ENERGY STORAGE↗
DOE OSTI2020

A Critical Review of Machine Learning of Energy Materials

Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

Chen, Chi↗