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DOE OSTI2020

A practical and efficient approach for Bayesian quantum state estimation

Bayesian inference is a powerful paradigm for quantum state tomography, treating uncertainty in meaningful and informative ways. Yet the numerical challenges associated with sampling from complex probability distributions hampers Bayesian tomography in practical settings. In this article, we introduce an improved, self-contained approach for Bayesian quantum state estimation. Leveraging advances in machine learning and statistics, our formulation relies on highly efficient preconditioned Crank–Nicolson sampling and a pseudo-likelihood. We theoretically analyze the computational cost, and provide explicit examples of inference for both actual and simulated datasets, illustrating improved performance with respect to existing approaches.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Scaling Ensembles of Data-Intensive Quantum Chemical Calculations for Millions of Molecules

Deep learning models are efficient computational tools that can accelerate the inverse design of molecules with desired functional properties by generating predictions at a fraction of the time required by traditional quantum chemical approaches. To ensure that a model maintains accuracy and transferability across broad regions of the chemical space explored during the inverse design, it must be trained on massively large volumes of simulation data. This requires running large-scale ensemble quantum chemical calculations on high-performance computing (HPC) systems for data collection. However, the efficient execution of such large ensemble calculations and the management of large volumes of output data require tools that can judiciously utilize computational resources and manage metadata overhead on the file system. Therefore, we present a high-performance, scalable, ensemble management framework for performing data-intensive quantum chemical electronic structure calculations for organic molecules. This framework provides abstractions to plug different ab initio, first principles, and first principles-based semi-empirical methods and executes them efficiently at large scale on HPC systems. It dynamically distributes tasks to resources and uses tiered storage for managing large collections of files. We employed this framework to process over ten million organic molecules and generate open-source datasets that provide UV-vis absorption spectra by running time-dependent density-functional tight-binding calculations. It is the largest database containing molecular optical spectra that were simulated with quantum chemical methods in a consistent manner.

Mehta, Kshitij↗

DOE OSTI2016

Investigation of high‐level waste glass melting using X‐ray computed tomography

Abstract A method has been developed to quantitatively assess the melting behavior of simulated nuclear waste glasses in a 5‐cm‐diameter stainless steel beaker heated from the bottom. The method applies X‐ray scanning and computed tomography to build three‐dimensional volumetric data of a heat‐treated sample and performs an adaptive segmentation analysis of the volumetric data to identify morphologically distinct regions in the sample matrix and quantify the amount of material in each region based on computed tomography density. The method was applied to two different series of simulated high‐level waste glass melter feeds, and the results showed that it provides detailed images of samples at various stages of melting, including distribution of gas bubbles of varying sizes within the sample matrix, as well as a quantitative measure of how fast various waste/frit feeds melted relative to each other. The results show that the melting rate is influenced by the rate of calcine gas evolution, melt viscosity, and the presence of modifier ions in the feed.

Choi, Alexander S.↗

NASA NTRS2005

Data-Driven Software Framework for Web-Based ISS Telescience

Software that enables authorized users to monitor and control scientific payloads aboard the International Space Station (ISS) from diverse terrestrial locations equipped with Internet connections is undergoing development. This software reflects a data-driven approach to distributed operations. A Web-based software framework leverages prior developments in Java and Extensible Markup Language (XML) to create portable code and portable data, to which one can gain access via Web-browser software on almost any common computer. Open-source software is used extensively to minimize cost; the framework also accommodates enterprise-class server software to satisfy needs for high performance and security. To accommodate the diversity of ISS experiments and users, the framework emphasizes openness and extensibility. Users can take advantage of available viewer software to create their own client programs according to their particular preferences, and can upload these programs for custom processing of data, generation of views, and planning of experiments. The same software system, possibly augmented with a subset of data and additional software tools, could be used for public outreach by enabling public users to replay telescience experiments, conduct their experiments with simulated payloads, and create their own client programs and other custom software.

Tso, Kam S.↗

DOE OSTI2020

A Learning-based Supervisory Control Architecture for Electric Vehicle Charging System Paired with Energy Storages

A Machine Learning-based predictive model is developed for optimal dispatching energy storage system integrated with Electric Vehicle battery charger. The model is effectively trained using transfer learning algorithm and successfully validated via measurement data to achieve high fidelity and accuracy. The study findings provide possibilities for future development of the presented approach to implement computational heavy predictive algorithms with multi-step predictions in emerging modular bidirectional charger topologies to realize vehicle-to-grid (V2G) technology. The online tuning can be explored using the new measurements obtained during the operation, henceforth, enhancing the controller performance for integrating multiple DERs into distribution power networks that shows huge interest for the utilities and regulatory agencies. Multiple operation scenatios have been evaluated and simulation results are discussed to verify intended performance of the proposed control solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Efficient Machine Learning Approach for Optimizing Scientific Computing Applications on Emerging HPC Architectures

Efficient parallel implementations of scientific applications on multi-core CPUs with accelerators such as GPUs and Xeon Phis is challenging. This requires - exploiting the data parallel architecture of the accelerator along with the vector pipelines of modern x86 CPU architectures, load balancing, and efficient memory transfer between different devices. It is relatively easy to meet these requirements for highly structured scientific applications. In contrast, a number of scientific and engineering applications are unstructured. Getting performance on accelerators for these applications is extremely challenging because many of these applications employ irregular algorithms which exhibit data-dependent control-ow and irregular memory accesses. Furthermore, these applications are often iterative with dependency between steps, and thus making it hard to parallelize across steps. As a result, parallelism in these applications is often limited to a single step. Numerical simulation of charged particles beam dynamics is one such application where the distribution of work and memory access pattern at each time step is irregular. Applications with these properties tend to present significant branch and memory divergence, load imbalance between different processor cores, and poor compute and memory utilization. Prior research on parallelizing such irregular applications have been focused around optimizing the irregular, data-dependent memory accesses and control-ow during a single step of the application independent of the other steps, with the assumption that these patterns are completely unpredictable. We observed that the structure of computation leading to control-ow divergence and irregular memory accesses in one step is similar to that in the next step. It is possible to predict this structure in the current step by observing the computation structure of previous steps. In this dissertation, we present novel machine learning based optimization techniques to address the parallel implementation challenges of such irregular applications on different HPC architectures. In particular, we use supervised learning to predict the computation structure and use it to address the control-ow and memory access irregularities in the parallel implementation of such applications on GPUs, Xeon Phis, and heterogeneous architectures composed of multi-core CPUs with GPUs or Xeon Phis. We use numerical simulation of charged particles beam dynamics simulation as a motivating example throughout the dissertation to present our new approach, though they should be equally applicable to a wide range of irregular applications. The machine learning approach presented here use predictive analytics and forecasting techniques to adaptively model and track the irregular memory access pattern at each time step of the simulation to anticipate the future memory access pattern. Access pattern forecasts can then be used to formulate optimization decisions during application execution which improves the performance of the application at a future time step based on the observations from earlier time steps. In heterogeneous architectures, forecasts can also be used to improve the memory performance and resource utilization of all the processing units to deliver a good aggregate performance. We used these optimization techniques and anticipation strategy to design a cache-aware, memory efficient parallel algorithm to address the irregularities in the parallel implementation of charged particles beam dynamics simulation on different HPC architectures. Experimental result using a diverse mix of HPC architectures shows that our approach in using anticipation strategy is effective in maximizing data reuse, ensuring workload balance, minimizing branch and memory divergence, and in improving resource utilization.

Arumugam, Kamesh↗

DOE OSTI2022

Hvac: Removing I/O Bottleneck for Large-Scale Deep Learning Applications

Scientific communities are increasingly adopting deep learning (DL) models in their applications to accelerate scientific discovery processes. However, with rapid growth in the computing capabilities of HPC supercomputers, large-scale DL applications have to spend a significant portion of training time performing I/O to a parallel storage system. Previous research works have investigated optimization techniques such as prefetching and caching. Unfortunately, there exist non-trivial challenges to adopting the existing solutions on HPC supercomputers for large-scale DL training applications, which include non-performance and/or failures at extreme scale, lack of portability and generality in design, complex deployment methodology, and being limited to a specific application or dataset. To address these challenges, we propose High-Velocity AI Cache (HVAC), a distributed read-cache layer that targets and fully exploits the node-local storage or near node-local storage technology. HVAC seamlessly accelerates read I/O by aggregating node-local or near node-local storage, avoiding metadata lookups and file locking while preserving portability in the application code. We deploy and evaluate HVAC on 1,024 nodes (with over 6000 NVIDIA V100 GPUS) of the Summit supercomputer. In particular, we evaluate the scalability, efficiency, accuracy, and load distribution of HVAC compared to GPFS and XFS-on-NVMe. With four different DL applications, we observe an average 25 % performance improvement atop GPFS and 9% drop against XFS-on-NVMe, which scale linearly and are considered the performance upper bound. We envision HVAC as an important caching library for upcoming HPC supercomputers such as Frontier.

Khan, Awais↗

NASA NTRS2012

Fiber Bragg Grating Sensor System for Monitoring Smart Composite Aerospace Structures

Lightweight, electromagnetic interference (EMI) immune, fiber-optic, sensor- based structural health monitoring (SHM) will play an increasing role in aerospace structures ranging from aircraft wings to jet engine vanes. Fiber Bragg Grating (FBG) sensors for SHM include advanced signal processing, system and damage identification, and location and quantification algorithms. Potentially, the solution could be developed into an autonomous onboard system to inspect and perform non-destructive evaluation and SHM. A novel method has been developed to massively multiplex FBG sensors, supported by a parallel processing interrogator, which enables high sampling rates combined with highly distributed sensing (up to 96 sensors per system). The interrogation system comprises several subsystems. A broadband optical source subsystem (BOSS) and routing and interface module (RIM) send light from the interrogation system to a composite embedded FBG sensor matrix, which returns measurand-dependent wavelengths back to the interrogation system for measurement with subpicometer resolution. In particular, the returned wavelengths are channeled by the RIM to a photonic signal processing subsystem based on powerful optical chips, then passed through an optoelectronic interface to an analog post-detection electronics subsystem, digital post-detection electronics subsystem, and finally via a data interface to a computer. A range of composite structures has been fabricated with FBGs embedded. Stress tensile, bending, and dynamic strain tests were performed. The experimental work proved that the FBG sensors have a good level of accuracy in measuring the static response of the tested composite coupons (down to submicrostrain levels), the capability to detect and monitor dynamic loads, and the ability to detect defects in composites by a variety of methods including monitoring the decay time under different dynamic loading conditions. In addition to quasi-static and dynamic load monitoring, the system can capture acoustic emission events that can be a prelude to structural failure, as well as piezoactuator-induced ultrasonic Lamb-waves-based techniques as a basis for damage detection.

Moslehi, Behzad↗

DOE OSTI2013

Investigation on the Core Bypass Flow in a Very High Temperature Reactor

Uncertainties associated with the core bypass flow are some of the key issues that directly influence the coolant mass flow distribution and magnitude, and thus the operational core temperature profiles, in the very high-temperature reactor (VHTR). Designers will attempt to configure the core geometry so the core cooling flow rate magnitude and distribution conform to the design values. The objective of this project is to study the bypass flow both experimentally and computationally. Researchers will develop experimental data using state-of-the-art particle image velocimetry in a small test facility. The team will attempt to obtain full field temperature distribution using racks of thermocouples. The experimental data are intended to benchmark computational fluid dynamics (CFD) codes by providing detailed information. These experimental data are urgently needed for validation of the CFD codes. The following are the project tasks: (1) Construct a small-scale bench-top experiment to resemble the bypass flow between the graphite blocks, varying parameters to address their impact on bypass flow. Wall roughness of the graphite block walls, spacing between the blocks, and temperature of the blocks are some of the parameters to be tested. (2) Perform CFD to evaluate pre- and post-test calculations and turbulence models, including sensitivity studies to achieve high accuracy. (3) Develop the state-of-the art large eddy simulation (LES) using appropriate subgrid modeling. (4) Develop models to be used in systems thermal hydraulics codes to account and estimate the bypass flows. These computer programs include, among others, RELAP3D, MELCOR, GAMMA, and GAS-NET. Actual core bypass flow rate may vary considerably from the design value. Although the uncertainty of the bypass flow rate is not known, some sources have stated that the bypass flow rates in the Fort St. Vrain reactor were between 8 and 25 percent of the total reactor mass flow rate. If bypass flow rates are on the high side, the quantity of cooling flow through the core may be considerably less than the nominal design value, causing some regions of the core to operate at temperatures in excess of the design values. These effects are postulated to lead to localized hot regions in the core that must be considered when evaluating the VHTR operational and accident scenarios.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Fiber optic computing using distributed feedback

Abstract The widespread adoption of machine learning and other matrix intensive computing algorithms has renewed interest in analog optical computing, which has the potential to perform large-scale matrix multiplications with superior energy scaling and lower latency than digital electronics. However, most optical techniques rely on spatial multiplexing, requiring a large number of modulators and detectors, and are typically restricted to performing a single kernel convolution operation per layer. Here, we introduce a fiber-optic computing architecture based on temporal multiplexing and distributed feedback that performs multiple convolutions on the input data in a single layer. Using Rayleigh backscattering in standard single mode fiber, we show that this technique can efficiently apply a series of random nonlinear projections to the input data, facilitating a variety of computing tasks. The approach enables efficient energy scaling with orders of magnitude lower power consumption than GPUs, while maintaining low latency and high data-throughput.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

A geospatial risk analysis graphical user interface for identifying hazardous chemical emission sources

Background: Performing back trajectory and forward trajectory using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) is a reliable approach for assessing particle transport after release among mid-field atmospheric models. HYSPLIT has an externally facing online interface that allows non-expert users to run the model trajectories without requiring extensive training or programming. However, the existing HYSPLIT interface is limited if simulations have a large amount of meteorological data and timesteps that are not coincident. The objective of this study is to design and develop a more robust tool to rapidly evaluate hazard transport conditions and to perform risk analysis, while still maintaining an intuitive and user-friendly interface. Methods: HYSPLIT calculates forward and backward trajectories of particles based on wind speed, wind direction, and the corresponding location, timestamp, and Pasquill stability classes of the regions of the atmosphere in terms of the wind speed, the amount of solar radiation, and the fractional cloud cover. The computed particle transport trajectories, combined with the online Proton Transfer Reaction-Mass Spectrometry (PTR-MS) data (https://figshare.com/articles/dataset/ARL_Data_from_PROS_station_at_Hanford_site/19993964), can be used to identify and quantify the sources and affected area of the hazardous chemicals’ emission using the potential source distribution function (PSDF). PSDF is an improved statistical function based on the well-known potential source contribution function (PSCF) in establishing the air pollutant source and receptor relationship. Performing this analysis requires a range of meteorological and pollutant concentration measurements to be statistically meaningful. The existing HYSPLIT graphical user interface (GUI) does not easily permit computations of trajectories of a dataset of meteorological data in high temporal frequency. To improve the performance of HYSPLIT computations from a large dataset and enhance risk analysis of the accidental release of material at risk, a geospatial risk analysis tool (GRAT-GUI) is created to allow large data sets to be processed instantaneously and to provide ease of visualization. Results: The GRAT-GUI is a native desktop-based application and can be run in any Windows 10 system without any internet access requirements, thus providing a secure way to process large meteorological datasets even on a standalone computer. GRAT-GUI has features to import, integrate, and convert meteorological data with various formats for hazardous chemical emission source identification and risk analysis as a self-explanatory user interface. The tool is available at https://figshare.com/articles/software/GRAT/19426742.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1985

Particle size, number, composition and velocity from solid rocket motors

The damage potentials to the space shuttle orbiter caused by the high velocity particles contained in the exhaust plume of an upper stage is discussed. In particular, the particle size distribution and composition, the velocity of the particles and the expected contribution from shuttle launched upper stages are addressed. Particle size estimates based on historical data are compapred with those derived from upper stage motor performance testing. The particle velocities as determined by the best available plume computational technique are presented. The shuttle is scheduled to launch approximately 135 upper-stages over its lifetime looking at the currently scheduled flights and averaging over a yearly basis yields the contribution of particulates from the uper-stages. On the average, 91,645 llbs of Al2O3 will be ejected on each launch. The analysis to determine how much of this 91,645 lbs will remain in orbit or the decay rate is yet to be accomplished.

Roberts, B. B.↗

NASA NTRS1989

Probability of failure and risk assessment of propulsion structural components

The probabilistic structural analysis method (PSAM) was developed to analyze the effects of fluctuating loads, variable material properties, and uncertain analytical models especially for high performance structures such as the Space Shuttle Main Engine turbopump blades. Risk is calculated after expensive service experience. However, probabilistic structural analysis provides a rational alternative method to quantify uncertainties in the structural performance and durability. NESSUS (Numerical Evaluation of Stochastic Structures Under Stress) was developed as a probabilistic structural analysis computer code which integrates finite element methods and reliability algorithms, capable to predicting the probability distributions of structural response variables such as stress, displacement, natural frequencies, and buckling loads. This computer code is detailed.

Shiao, Michael C.↗

NASA NTRS2008

Transient Three-Dimensional Startup Side Load Analysis of a Regeneratively Cooled Nozzle

The objective of this effort is to develop a computational methodology to capture the startup side load physics and to anchor the computed aerodynamic side loads with the available data from a regeneratively cooled, high-aspect-ratio nozzle, hot-fired at sea level. The computational methodology is based on an unstructured-grid, pressure-based, reacting flow computational fluid dynamics and heat transfer formulation, a transient 5 s inlet history based on an engine system simulation, and a wall temperature distribution to reflect the effect of regenerative cooling. To understand the effect of regenerative wall cooling, two transient computations were performed using the boundary conditions of adiabatic and cooled walls, respectively. The results show that three types of shock evolution are responsible for side loads: generation of combustion wave; transitions among free-shock separation, restricted-shock separation, and simultaneous free-shock and restricted shock separations; along with the pulsation of shocks across the lip, although the combustion wave is commonly eliminated with the sparklers during actual test. The test measured two side load events: a secondary and lower side load, followed by a primary and peak side load. Results from both wall boundary conditions captured the free-shock separation to restricted-shock separation transition with computed side loads matching the measured secondary side load. For the primary side load, the cooled wall transient produced restricted-shock pulsation across the nozzle lip with peak side load matching that of the test, while the adiabatic wall transient captured shock transitions and free-shock pulsation across the lip with computed peak side load 50% lower than that of the measurement. The computed dominant pulsation frequency of the cooled wall nozzle agrees with that of a separate test, while that of the adiabatic wall nozzle is more than 50% lower than that of the measurement. The computed teepee-like formation and the tangential motion of the shocks during lip pulsation also qualitatively agree with those of test observations. Moreover, a third transient computation was performed with a proportionately shortened 1 s sequence, and lower side loads were obtained with the higher ramp rate.

Wang, Ten-See↗

NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.

Jan Stupl↗

NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

Jan Stupl↗

NASA NTRS1991

Rotating reactor studies

Undesired gravitational effects such as convection or sedimentation in a fluid can sometimes be avoided or decreased by the use of a closed chamber uniformly rotated about a horizontal axis. In a previous study, the spiral orbits of a heavy or buoyant particle in a uniformly rotating fluid were determined. The particles move in circles, and spiral in or out under the combined effects of the centrifugal force and centrifugal buoyancy. A optimization problem for the rotation rate of a cylindrical reactor rotated about its axis and containing distributed particles was formulated and solved. Related studies in several areas are addressed. A computer program based on the analysis was upgraded by correcting some minor errors, adding a sophisticated screen-and-printer graphics capability and other output options, and by improving the automation. The design, performance, and analysis of a series of experiments with monodisperse polystyrene latex microspheres in water were supported to test the theory and its limitations. The theory was amply confirmed at high rotation rates. However, at low rotation rates (1 rpm or less) the assumption of uniform solid-body rotation of the fluid became invalid, and there were increasingly strong secondary motions driven by variations in the mean fluid density due to variations in the particle concentration. In these tests the increase in the mean fluid density due to the particles was of order 0.015 percent. To a first approximation, these flows are driven by the buoyancy in a thin crescent-shaped depleted layer on the descending side of the rotating reactor. This buoyancy distribution is balanced by viscosity near the walls, and by the Coriolis force in the interior. A full analysis is beyond the scope of this study. Secondary flows are likely to be stronger for buoyant particles, which spiral in towards the neutral point near the rotation axis under the influence of their centrifugal buoyancy. This is because the depleted layer is thicker and extends all the way around the reactor.

Roberts, Glyn O.↗

DOE OSTI2016

Thermodynamics, Kinetics and Structural Evolution of ε-LiVOPO 4 over Multiple Lithium Intercalation

ε -Li x VOPO 4 is a promising multi-electron cathode for rechargeable lithium-ion bat- teries that has an extremely high theoretical capacity of 318 mAh/g. In this w ork, we demonstrate the stable cycling of more than one Li in solid-state-syn thesized ε - LiVOPO 4 over more than 20 cycles for the first time. Using a combination of densit y functional theory (DFT) calculations, X-ray pair distribution funct ion (PDF) analy- sis and X-ray Absorption Near Edge Structure (XANES) measurements, we pre sent a comprehensive analysis of the thermodynamics, kinetics and stru ctural evolution of ε -Li x VOPO 4 over the entire lithiation range. We identify two intermediate ph ases at x = 1 . 5 and 1.75 in the low-voltage regime using DFT calculations, and the comput ed and electrochemically measured voltage profiles are in excellent agree ment. Operando PDF techniques show a reversible hysteretic change in the short ( < 2° A) V-O bond lengths coupled with an irreversible extension of the long V-O bond ( > 2.4 °A) dur- ing low-voltage cycling. These observations are confirmed with EXAFS spe ctra. We identify hydrogen intercalation from the electrolyte decomposition as a likely explana- tion for the ~ 2 . 4°A V-O bond and its irreversible extension. In terms of electronic conductivity, we find Li x VOPO 4 to be a large band gap insulator across the entire lithiation range, and calculated small polaron migration barriers are similar to those of the olivine LiMPO 4 cathodes. Finally, we demonstrate that ε -LiVOPO 4 is likely to be a one-dimensional diffuser using climbing-image nudged elastic ban d calculations. These results highlight the importance of nano-sizing and carbon coating in achieving good electrochemical performance in this material.

Lin, Yuh-Chieh↗