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DOE OSTI2024

Synthesis of Microscopic 3D Graphene for High‐Performance Supercapacitors with Ultra‐High Areal Capacitance

Abstract Despite graphene being considered an ideal supercapacitor electrode material, its use in commercial devices is limited because few methods exist to produce high‐quality graphene at a large scale and low cost. A simple method is reported to synthesize 3D graphene by graphenization of coal tar pitch with a K 2 CO 3 catalyst. This produces 3D graphenes with high specific surface areas up to 2113 m 2 g −1 and exceptional crystallinity (Raman I D / I G as low as ≈0.15). The material has an outstanding specific capacitance of 182.6 F g −1 at a current density of 1.0 A g −1 . This occurs at a mass loading of 30 mg cm −2 which is 3 times higher than commercial requirements, yielding an ultra‐high areal capacitance of 5.48 F cm −2 . The K 2 CO 3 is recycled and reused over 10 cycles with material quality and electrocapacitive performance of 3D graphene retained and verified after each cycle. The synthesis method and resulting electrocapacitive performance properties create new opportunities for using 3D graphene more broadly in practical supercapacitor devices.

36 MATERIALS SCIENCE↗

DOE OSTI2022

aphBO-2GP-3B: a budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture

High-fidelity complex engineering simulations are often predictive, but also computationally expensive and often require substantial computational efforts. The mitigation of computational burden is usually enabled through parallelism in high-performance cluster (HPC) architecture. Optimization problems associated with these applications is a challenging problem due to the high computational cost of the high-fidelity simulations. In this paper, an asynchronous parallel constrained Bayesian optimization method is proposed to efficiently solve the computationally expensive simulation-based optimization problems on the HPC platform, with a budgeted computational resource, where the maximum number of simulations is a constant. The advantage of this method are three-fold. Firstly, the efficiency of the Bayesian optimization is improved, where multiple input locations are evaluated parallel in an asynchronous manner to accelerate the optimization convergence with respect to physical runtime. This efficiency feature is further improved so that when each of the inputs is finished, another input is queried without waiting for the whole batch to complete. Second, the proposed method can handle both known and unknown constraints. Third, the proposed method samples several acquisition functions based on their rewards using a modified GP-Hedge scheme. The proposed framework is termed aphBO-2GP-3B, which means asynchronous parallel hedge Bayesian optimization with two Gaussian processes and three batches. The numerical performance of the proposed framework aphBO-2GP-3B is comprehensively benchmarked using 16 numerical examples, compared against other 6 parallel Bayesian optimization variants and 1 parallel Monte Carlo as a baseline, and demonstrated using two real-world high-fidelity expensive industrial applications. The first engineering application is based on finite element analysis (FEA) and the second one is based on computational fluid dynamics (CFD) simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Matrix-Free High-Performance Saddle-Point Solvers for High-Order Problems in $\boldsymbol{H}(\operatorname{\textbf{div}})$

Here, this work describes the development of matrix-free GPU-accelerated solvers for high-order finite element problems in H(div). The solvers are applicable to grad-div and Darcy problems in saddle-point formulation, and have applications in radiation diffusion and porous media flow problems, among others. Using the interpolation–histopolation basis, efficient matrix-free preconditioners can be constructed for the (1, 1)-block and Schur complement of the block system. With these approximations, block-preconditioned MINRES converges in a number of iterations that is independent of the mesh size and polynomial degree. The approximate Schur complement takes the form of an M-matrix graph Laplacian and therefore can be well-preconditioned by highly scalable algebraic multigrid methods. High-performance GPU-accelerated algorithms for all components of the solution algorithm are developed, discussed, and benchmarked. Numerical results are presented on a number of challenging test cases, including the “crooked pipe” grad-div problem, the SPE10 reservoir modeling benchmark problem, and a nonlinear radiation diffusion test case.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Queue wait time prediction in high performance computing (HPC) systems

High Performance Computing (HPC) systems are critical enablers for groundbreaking scientific research across various domains. Efficient resource allocation, facilitated by job scheduling, is paramount for maximizing the utilization of HPC systems. However, the variability in wait times for queued jobs poses challenges for users, necessitating accurate job wait time estimation. This paper explores the influence of job characteristics, including job size (the number of nodes requested and walltime), the queue to which the job is submitted and other resource requirements, on job wait times in leadership-class HPC systems. Focusing on the Theta Cray XC40 and Polaris machines at Argonne National Laboratory, the study evaluates the performance of different supervised learning algorithms in predicting job wait times. It also evaluates the impact of data preprocessing, including outlier detection, Principal Component Analysis (PCA), and feature selection, on the performance of wait time prediction models. The findings reveal insights into the relationship between job characteristics and wait times, offering a foundation for optimizing resource allocation and enhancing user experience. The methodologies and tools developed in this study are adaptable to other leadership-class HPC systems, providing a valuable contribution to the broader HPC community aiming to improve job scheduling efficiency and user satisfaction.

Okafor, Nwamaka↗

DOE OSTI2023

Auto-HPCnet: An Automatic Framework to Build Neural Network-based Surrogate for High-Performance Computing Applications

High-performance computing communities are increasingly adopt- ing Neural Networks (NN) as surrogate models in their applications to generate scientific insights. Replacing an execution phase in the application with NN models can bring significant performance im- provement. However, there is a lack of tools that can help domain scientists automatically apply NN-based surrogate models to HPC applications. We introduce a framework, named Auto-HPCnet, to democratize the usage of NN-based surrogates. Auto-HPCnet is the first end-to-end framework that makes past proposals for the NN-based surrogate model practical and disciplined. Auto-HPCnet introduces a workflow to address unique challenges when apply- ing the approximation, such as feature acquisition and meeting the application-specific constraint on the quality of final computation outcome. We show that Auto-HPCnet can leverage NN for a set of HPC applications and achieve 5.50× speedup on average (up to 16.8× speedup and with data preparation cost included) while meeting the application-specific constraint on the final computation quality.

Dong, Wenqian↗

DOE OSTI2024

Data Center Immersion Cooling: A Case Study and Summary of High-Performance Computing Cooling Technologies

The future of High Performance Computing (HPC) is carved out for all high-performance facilities. As computer processing increases exponentially, especially with the demand for AI training, more speed will equate to more computing density and ultimately more power draw and resource demand. Sandia National Laboratories has not seen or known of anything that would lead us to think this might change in the next 5 to 10 years.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

High-Performance Accelerator Modeling: Toward Improving Controls and Diagnostics for High-Brightness Beams in Experiment

One of the main charges of beam physics is to improve the overall beam quality delivered to experiments and applications, which is generally quantified by the beam brightness. High brightness beams have numerous applications, including x-ray free electron lasers and ultrafast electron diffraction (UED). To these ends, this thesis details efforts to use high-performance models --- high-fidelity models that execute quickly --- for controls and diagnostics. To demonstrate the generality of these techniques, this thesis focuses on three of the most successful photoinjector designs currently used worldwide: the UCLA/SLAC/BNL-type high-gradient S-band gun, the continuous-wave high-repetition rate VHF APEX gun and the L-band DESY-PITZ-type gun. Work will be shown from Pegasus (UCLA), HiRES (LBNL) and FAST (FNAL).Specifically, data-driven models for online virtual diagnostics are presented, in this case, in the context of UED at HiRES, leading to a temporal resolution improvement. Methods for improving the fidelity of physics-based models are discussed, with examples at HiRES, Pegasus and FAST. Markov-chain Monte Carlo analysis is applied to match simulations, in the context of photocathode studies at HiRES and Pegasus. Lastly, the augmentation of online model-based predictions with model-independent optimization is explored in a fluctuating environment at Pegasus and HiRES. A central theme of this dissertation is working with parameter fluctuations when modeling an accelerator beamline. Long-term drifts and shot-to-shot jitter exist in every accelerator to a varying degree and therefore play an important role in every chapter of this dissertation. This thesis attempts to address the issues associated with these fluctuations when trying to develop faithful model representations of the system.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Exploratory analysis and performance prediction of big data transfer in High-performance Networks

Big data transfer in large-scale scientific and business applications is increasingly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) via advance bandwidth reservation. Provisioning agents need to carefully schedule data transfer requests, compute network paths, and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized, could be simply wasted due to the exclusive access during the approved time window, and cause extra overhead and complexity for resource management. This calls for accurate performance prediction to reserve bandwidths that match actual needs and avoid over-provisioning. We employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements collected in the past several years from data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated testbeds. We first analyze the performance patterns in response to a comprehensive list of parameters in end-host systems, network connections, and data transfer applications, which motivate the use of machine learning and also help us identify the effects of latent factors. We then propose threshold- and clustering-based methods to eliminate negative effects of latent factors in data preprocessing and build a robust performance predictor based on customized domain-oriented loss functions. The performance of the proposed methods is verified by extensive experiments using SVR and RFR as well as theoretical analysis of the general performance bound.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Development of high-performance long-pulse discharge in KSTAR

High-performance long-pulse plasma operation is essential for producing economically viable fusion energy in tokamak devices. To achieve such discharges in KSTAR, firstly, the rapid increase in the temperature of plasma-facing components was mitigated. The temperature increase of the poloidal limiter, especially, was associated with beam-driven fast ion orbit loss and the discrepancy of the equilibrium reconstructed with heated magnetic probes of signal drift. The fast ions lost to the poloidal limiter were reduced by optimizing the plasma shape and the composition of neutral beam injection (NBI). This nonlinear signal drift was successfully reduced by a new thermal shielding protector on the magnetic probes. Secondly, a lower loop voltage approach was implemented to reduce a poloidal flux consumption rate. A plasma current of 400 kA and a line-averaged electron density of ~2.0 × 10 19 m –3 were chosen by considering the L–H power threshold, fast ion orbit loss, and beam shine-through power loss for low loop voltage in KSTAR. In addition, the application of electron cyclotron heating also helped maintain the plasma with low loop voltage (~25 mV) by enhancing the NBI-driven current and achieving a high poloidal beta (β P ) state. KSTAR has achieved a long pulse (~90 s) operation with the high performance of β P $\leqslant$ 2.7, thermal energy confinement enhancement factor (H 98y2 ) ~ 1.1, and fraction of non-inductive current (f NI ) $\leqslant$ 0.96. Still, gradual degradation of the plasma performance has been observed over time in the discharges. In one of the long-pulse discharges, β P reduced by ~18% over the time of ~8τ R (current relaxation time, τ R ~5 s) and ~1067τ E,th (thermal energy confinement time, τ E,th ~45 ms). The degradation may be closely associated with weak, yet growing, and persistent toroidal Alfvén eigenmodes and their effect on fast ion confinement.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Performance Analysis of an Optimization Algorithm for Metamaterial Design on the Integrated High-Performance Computing and Quantum Systems

Optimizing metamaterials with complex geometries is a big challenge. Although an active learning algorithm, combining machine learning (ML), quantum computing, and optical simulation, has emerged as an efficient optimization tool, it still faces difficulties in optimizing complex structures that have potentially high performance. In this work, we comprehensively analyze the performance of an optimization algorithm for metamaterial design on the integrated HPC and quantum systems. We demonstrate significant time advantages through message-passing interface (MPI) parallelization on the high-performance computing (HPC) system showing approximately 54% faster ML tasks and 67 times faster optical simulation against serial workloads. Furthermore, we analyze the performance of a quantum algorithm designed for optimization, which runs with various quantum simulators on a local computer or HPC-quantum system. Results showcase ~24 times speedup when executing the optimization algorithm on the HPC-quantum hybrid system. This study paves a way to optimize complex metamaterials using the integrated HPC-quantum system.

Kim, Seongmin↗

DOE OSTI2024

Achieving stationary high performance plasmas at Wendelstein 7-X

This work reports on recent results on the search for high performance plasma scenarios at the magnetically confined stellarator fusion device Wendelstein 7-X. In four new designed scenarios, the development from transient toward stationary plasmas of improved performance has been realized. In particular, a high performance duration of up to 5 s, an energy confinement time of 0.3 s, a diamagnetic energy of 1.1 MJ, a central ion temperature of 2.2 keV, and a fusion triple product of 3.4 x 10 19 m -3 ∙ keV ∙ s have been achieved, and previously observed limitations of the machine have been overcome, regarding both the performance and its duration. The two main experimental techniques for stationary high performance are neutral beam injection core fueling on the one hand and the use of a magnetic field configuration with internal islands on the other hand. Two of the developed scenarios are expected to be extendable straightforward toward a duration of several tens of seconds, making use of the long pulse operation capabilities of W7-X.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

The Building Business Network (B-Biz): Addressing Gaps in the High-Performance Building Technology Market, Especially for Underserved Customers

Increased market intelligence and business model innovation is needed to build contractor confidence in high-performance building technologies in order to increase the speed and scale of adoption to meet the U.S. Department of Energy's building stock decarbonization goals. Although there have been important innovations in energy efficiency, affordability, and decarbonization of building technologies, consumers are not purchasing these technologies at the necessary speed and scale partially due to a gap in institutional understanding around barriers contractors face in providing and servicing these technologies. Small businesses in this market must mitigate risk around high-performance building technologies, limiting their opportunities in the market and impacting the rate of adoption. The Building Business Network (B-Biz) aims to provide high-performance building technology solutions to underserved customers by collaborating with local small businesses that provide and service high-performance building technologies in communities with the lowest rates of adoption. This research explores the current market, the importance of business models, and the opportunity to utilize small businesses to address market gaps in underserved communities.

B-Biz↗

DOE OSTI2021

Development and characteristics of ultra high-performance lightweight cementitious composites (UHP-LCCs)

Highlights: • An ultra high-performance lightweight cementitious composite (UHP-LCC) was designed. • The UHP-LCCs had ultra high strength (>120 MPa) and very low density (3 ). • The UHP-LCCs had high structural efficiency, good functional properties and superior durability. • The strategies and mechanisms on achieving UHP-LCCs with excellent performance were proposed. High strength and light weight are two recent opposite development trends of concrete. This study proposed a design concept of an ultra high-performance lightweight cementitious composite (UHP-LCC), which had a compressive strength of higher than 120 MPa and an air-dried density down to around 1800 kg/m{sup 3}. The UHP-LCCs were innovatively developed by incorporating micro-sized hollow particles with a high strength shell (hollow glass microspheres, HGM) into an ultra-high performance cementitious composite (UHPC). The roles of HGM in the UHP-LCCs were investigated by evaluating the reactivity of the HGM and the mechanisms on achieving the excellent mechanical properties, low density and superior durability were revealed. The Chapelle test results showed that the HGM exhibited some pozzolanic reactivity, which facilitated the reaction between the shell of HGM and the alkali hydration products of the paste matrix. This chemical reaction was conducive to improving the HGM-paste interface and enhancing the mechanical properties. With the use of microspheres with a high stiff shell, the fundamental properties of the UHP-LCCs including thermal insulation, sound absorption, resistance to water ingress and electrical resistivity were improved significantly. The strategies for preparing the UHP-LCCs with high structural efficiency and great performance were proposed. The results of this study provide a new approach for designing and producing a lightweight UHPC, which would be a promising material for long-span structures.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Ordered CoPt oxygen reduction catalyst with high performance and durability

A high-performance and durable polymer electrolyte membrane fuel cell cathode catalyst composed of an ordered L10–CoPt core and a thin Pt shell was designed and prepared. Under practical fuel cell membrane electrode assembly testing conditions, the cathode catalyst showed an outstanding initial mass activity of 0.6 A/mgPt, which satisfies the U.S. Department of Energy performance target while also meeting the durability target of less than 40% loss in mass activity after 30,000 accelerated stress test voltage cycles. The high structural stability of the L10–CoPt@Pt-shell catalyst was confirmed by postmortem materials characterization, and the origin of this robustness was revealed by density functional theory calculations, where the barriers for diffusion of Co atoms were observed to be significantly increased in the ordered intermetallic core.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Redefining precision interferometry and spectroscopy with high-performance optical interference coatings

High-performance optical interference coatings have transformed precision interferometry and spectroscopy by enabling unparalleled control over light–matter interactions. This review explores recent innovations in ion-beam sputtered amorphous dielectric, as well as substrate-transferred crystalline coatings, and their impact on systems at the forefront of precision metrology. These state-of-the-art coating techniques generate multilayers with ultralow optical losses, yielding mirrors with exceptional reflectivity. Refinements in their noise performance push the ultimate limits of sensitivity, resolution, and stability in demanding laser-based metrology applications. These technologies underpin the most advanced timekeeping and spatial measurement tools, enabling high-finesse reference cavities for the world’s most precise optical atomic clocks and low-noise reflective test masses for km-baseline gravitational-wave detectors. Emerging hybrid designs combining these techniques expand access to the mid-infrared spectral region, enabling the first ultralow-optical-loss coatings in the 3000–5000 nm wavelength range for enhanced spectroscopy and trace-gas detection. We highlight how these technologies redefine coating performance metrics and set new benchmarks in quantum science, fundamental physics, and precision optical sensing.

Cole, Garrett D. [University of Arizona, Tucson, A↗

DOE OSTI2024

High–Performance NiCo 2 O 4 /Graphene Quantum Dots for Asymmetric and Symmetric Supercapacitors with Enhanced Energy Efficiency

For the sustainable growth of future generations, energy storage technologies like supercapacitors and batteries are becoming more and more common. However, reliable and high-performance materials’ design and development is the key for the widespread adoption of batteries and supercapacitors. Quantum dots with fascinating and unusual properties are expected to revolutionize future technologies. However, while the recent discovery of quantum dots honored with a Nobel prize in Chemistry, their benefits for the tenacious problem of energy are not realized yet. In this context, herein, chemical-composition tuning enabled exceptional performance of NiCo 2 O 4 (NCO)/graphene quantum dots (GQDs) is reported, which outperform the existing similar materials, in supercapacitors. A comprehensive study is performed on the synthesis, characterization, and electrochemical performance evaluation of highly functional NCO/GQDs in supercapacitors delivering enhanced energy efficiency. The high-performance, functional NCO/GQDs electrode materials are synthesized by the incorporation of GQDs into NCO. The effect of variable amount of GQDs on the energy performance characteristics of NCO/GQDs in supercapacitors is studied systematically. In-depth structural and chemical bonding analyses using X-ray diffraction (XRD) and Raman spectroscopic studies indicate that all the NCO/GQDs composites crystallize in the spinel cubic phase of NiCo 2 O 4 while graphene integration evident in all the NCO/GQDs. The scanning electron microscopy imaging analysis reveals homogeneously distributed spherical particles with a size distribution of 5–9 nm validating the formation of QDs. The high-resolution transmission electron microscopy analyses reveal that the NCOQDs are anchored on graphene sheets, which provide a high surface area of 42.27 m 2 g –1 and high mesoporosity for the composition of NCO/GQDs-10%. In addition to establishing reliable electrical connection to graphene sheets, the NCOQDs provide reliable 3D-conductive channels for rapid transport throughout the electrode as well as synergistic effects. Chemical-composition tuning, and optimization yields NCO/GQDs-10% to deliver the best specific capacitance of 3940 Fg –1 at 0.5 Ag –1 , where the electrodes retain ≈98% capacitance after 5000 cycles. The NCO/GQD-10%//AC asymmetric supercapacitor device demonstrates outstanding energy density and power density values of 118.04 Wh kg –1 and 798.76 W kg –1 , respectively. The NCO/GQDs-10%//NCO/GQDs-10% symmetric supercapacitor device delivers excellent energy and power density of 24.30 Wh kg –1 and 500 W kg –1 , respectively. These results demonstrate and conclude that NCO/GQDs are exceptional and prospective candidates for developing next-generation high-performance and sustainable energy storage devices.

25 ENERGY STORAGE↗

DOE OSTI2021

Thickness-independent scalable high-performance Li-S batteries with high areal sulfur loading via electron-enriched carbon framework

Abstract Increasing the energy density of lithium-sulfur batteries necessitates the maximization of their areal capacity, calling for thick electrodes with high sulfur loading and content. However, traditional thick electrodes often lead to sluggish ion transfer kinetics as well as decreased electronic conductivity and mechanical stability, leading to their thickness-dependent electrochemical performance. Here, free-standing and low-tortuosity N, O co-doped wood-like carbon frameworks decorated with carbon nanotubes forest (WLC-CNTs) are synthesized and used as host for enabling scalable high-performance Li-sulfur batteries. EIS-symmetric cell examinations demonstrate that the ionic resistance and charge-transfer resistance per unit electro-active surface area of S@WLC-CNTs do not change with the variation of thickness, allowing the thickness-independent electrochemical performance of Li-S batteries. With a thickness of up to 1200 µm and sulfur loading of 52.4 mg cm −2 , the electrode displays a capacity of 692 mAh g −1 after 100 cycles at 0.1 C with a low E/S ratio of 6. Moreover, the WLC-CNTs framework can also be used as a host for lithium to suppress dendrite growth. With these specific lithiophilic and sulfiphilic features, Li-S full cells were assembled and exhibited long cycling stability.

36 MATERIALS SCIENCE↗

DOE OSTI2022

2,3-Diphenylthieno[3,4-b]pyrazines as Hole-Transporting Materials for Stable, High-Performance Perovskite Solar Cells

High-performance and durable perovskite solar cells (PSCs) have advanced rapidly, enabled in part by the development of superior interfacial hole-transporting layers (HTLs). Here, in this study, a new series of 2,3-diphenylthieno[3,4-b]pyrazine (DPTP)-based small molecules containing bis- and tetrakis-triphenyl amino donors (1–3) was synthesized from simple, low-cost, and readily available starting materials. The matched energy levels, ideal surface topographies, high hole mobilities of 8.57 × 10 –4 cm 2 V –1 S –1 , and stable chemical structures of DPTP-4D (3) make it an effective hole-transporting material, delivering a PCE of 20.18% with high environmental, thermal, and light-soaking stability when compared to the reference HTL materials, doped Spiro-OMeTAD and PTAA in PSC n-i-p planar devices. Overall, these DPTP-based molecules are promising HTM candidates for the fabrication of stable PSCs.

14 SOLAR ENERGY↗