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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Next Generation Logistics Systems for Delivering Optimal Biomass Feedstocks to Biorefining Industries in the Southeastern U.S.

The diverse portfolio of biomass sources that is available in the Southeastern U.S., including a significant supply of pine “residue”, represents a valuable strategic position for the region. Through blends formulated based on critical properties, this project will take full advantage of the range in biomass properties afforded by the portfolio to produce a consistent, high-performance feedstock for the industry, while lowering cost. Key developments being targeted to enable this potential include whole-tree transport to a state-of-the-art merchandising depot that will further access biomass from ongoing, forest industry operations. The approach will more effectively utilize the tree and distribute cost, while minimizing in-woods contamination of the woody biomass component. To implement this vision, information on the chemical composition and changes that are induced during multiple preprocessing steps (size reduction, moisture removal, densification, etc.) is needed. New NIR sensor technology will be developed for online monitoring of important biomass properties. The data will be incorporated into a statistical process control platform to improve process efficiency and meet required specifications. Advanced process models are being developed to inform the techno-economic and life-cycle assessment of the program’s impact. The new system will ultimately reduce operational risks from supply chain disruptions, and allow operation of larger-scale biorefineries.

09 BIOMASS FUELS↗
DOE OSTI2018

Tartan: Evaluating Modern GPU Interconnect via a Multi-GPU Benchmark Suite

In this paper, we fill the gap by proposing a multi-GPU benchmark suite named Tartan, which contains microbenchmarks, scale-up and scale-out applications. We then apply Tartan to evaluate the four latest types of modern GPU interconnects, i.e., PCI- e, NVLink-V1, NVLink-V2 and InfiniBand with GPUDirect- RDMA from two recently released NVIDIA super AI platforms as well as ORNL’s exascale prototype system. Based on empirical evaluation, we observe four new types of NUMA effects: three types are triggered by NVLink’s topology, connectivity and routing, while one type is caused by PCI-e (i.e., anti-locality). They are very important for performance tuning in multi-GPU environment. Our evaluation results show that, unless the current CPU-GPU master-slave programming model can be replaced, it is difficult for scale-up multi-GPU applications to really benefit from faster intra-node interconnects such as NVLinks; while for inter-node scale-out applications, although interconnect is more crucial to the overall performance, GPUDirect-RDMA appears to be not always the optimal choice.

HPC↗
DOE OSTI2019

Applying Machine Learning to Understand Write Performance of Large-scale Parallel Filesystems

In high-performance computing (HPC), I/O performance prediction offers the potential to improve the efficiency of scientific computing. In particular, accurate prediction can make runtime estimates more precise, guide users toward optimal checkpoint strategies, and better inform facility provisioning and scheduling policies. HPC I/O performance is notoriously difficult to predict and model, however, in large part because of inherent variability and a lack of transparency in the behaviors of constituent storage system components. In this work we seek to advance the state of the art in HPC I/O performance prediction by (1) modeling the mean performance to address high variability, (2) deriving model features from write patterns, system architecture and system configurations, and (3) employing Lasso regression model to improve model accuracy. We demonstrate the efficacy of our approach by applying it to a crucial subset of common HPC I/O motifs, namely, file-per-process checkpoint write workloads. We conduct experiments on two distinct production HPC platforms—Titan at the Oak Ridge Leadership Computing Facility and Cetus at the Argonne Leadership Computing Facility—to train and evaluate our models. We find that we can attain ≤ 30% relative error for 92.79% and 99.64% of the samples in our test set on these platforms, respectively.

Xie, Bing↗
DOE OSTI2019

Transmission Expansion Planning Test System for AC/DC Hybrid Grid With High Variable Renewable Energy Penetration

Test systems are important tools and benchmark for power system research. Currently, there is a lack of standard test systems for modern transmission expansion planning (TEP) research, especially under high variable renewable energy (VRE) penetrations. This article describes a 38-bus test system named the HRP-38 system dedicated to TEP. “HRP” stands for high renewable penetration. The objective of establishing such a system is to provide a consistent platform for different TEP methods to be tested and compared. The test system considers high VRE penetration of more than 30% energy share, which will be an important feature in many power systems in the future. Both AC and DC candidate lines are given to provide sufficient transmission planning alternatives. The complexity of the test system is well balanced considering calculation tractability and the ability to test the performance of the TEP planning model. The network topology, generation mix and load characteristics are described in detail. Year-round time series of VRE (wind power and PV) output and load curves for each bus are provided to facilitate a detailed evaluation of the transmission grid configuration under high VRE penetration. Finally, four benchmark results with different optimization settings are provided for the HRP-38 system.

AC/DC hybrid↗
DOE OSTI2021

IC reports (2021)

We requested HPC support to continue research on the seismic waves generated by impacts. We used the following codes: (1) the Hybrid Optimization Software Suite (HOSS), developed at LANL. HOSS is based on a combined Finite and Discrete Element Method (FDEM). New material models are developed for the sedimentary rocks. HOSS has been recently benchmarked to iSale and FLAG codes. SPECFEM3D is an open-source code developed since the last 90s. It won the Gordon Bell award for best performance in 2003, was finalist again in 2008 for a run at 0.16 petaflops on 149,784 cores on the ‘Jaguar’ Cray system at Oak Ridge National Laboratory. It also won the BULL Joseph Fourier supercomputing award in 2010.; SW4 is a 4th-order finite difference code developed at LLNL which is currently actively developed to handle complex 3D models and to be ported on future exascale platforms. We assessed our need to a total of 3.9M CPU-hrs for year 1 and and 3.1 M for year 2.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

System Integration of Rationally Designed Dilute Alloy Catalysts for Energy-Efficient Electrochemical CO 2 -To-Fuel Conversion

Energy effiecient electrochemical conversion of CO 2 to high-demand chemicals and transportation fuels using renewable solar and wind energy is a key technology needed for a high-productivity, low-carbon future. However, the development of scalable, low-cost, active, selective, and stable electrocatalysts remains a key challenge that needs to be overcome to enable high-volume conversion of CO 2 to feedstock chemicals for the chemical industry. In previous work, Lawrence Livermore National Laboratory (LLNL) has developed a rational design platform for dilute alloy transition metal electrocatalysts that promise to make electrochemical CO 2 conversion more energy efficient and selective. In this project, we worked with our industrial partners, Twelve and TotalEnergies, to improve scale up, integration, and stability of LLNL’s dilute alloy catalyst technology into an industry-relevant zero gap electrolyzer platform. Through virtual experiments and data analysis, we designed efficient and cost-effective copper-based catalysts. The catalyst was specifically designed to streamline the slowest and most energy-intensive step of the electrochemical chemical transformation of CO 2 to multi-carbon products – that is making the carbon-carbon bond by dimerization of the reaction intermediate carbon monoxide - resulting in up to 10% improvement in energy efficiency for C 2 products while simultaneously increasing the selectivity towards C 2 products. We tested two different scalable catalyst coating technologies and down-selected magnetron sputtering as the technique that provided the best control over catalyst loading, composition, and morphology. Using this technology, we successfully demonstrated integration of our dilute alloy catalysts into a 100 cm 2 electrolyzer platform with Faradaic efficiencies for ethylene production reaching 40% at a current density of 200 mA/cm 2 . We also developed the technology to integrate a well-defined nanoscale porosity by depositing alloy compositions that were compatible with dealloying, that is, selective leaching of an alloy component to generate nanoscale porosity. We observed that integration of the dealloying-derived nanoporosity improved catalyst performance and stability by leading to a more hydrophobic catalyst/anion exchange membrane interface. Unsolved problems that still need to be addressed are corrosion of the Cu catalyst -specifically if the used catalyst is exposed to air - as well as long term stability do to salt formation/deposition and flooding of the catalyst/electrolyzer flow channels, especially at higher current densities. As we only worked on optimization of catalyst composition, coating thickness, and morphology, further performance optimization will require a system level approach that includes optimization of electrolyzer design and membrane technology.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Comprehensive methods to enhance the electrochemical performances of LiFe{sub 0.94}Mg{sub 0.03}Cu{sub 0.03}PO{sub 4}/C cathode for lithium ion batteries

Graphical abstract: Relationships between the conductivities of LFPC-2 cathode and the volume percentages of AB (a) and PAn (b). - Highlights: • LiFe{sub 0.94}Mg{sub 0.03}Cu{sub 0.03}PO{sub 4}/C is synthesized by adding glucose with two-step method. • Conductive polymer polyaniline is used to replace acetylene black. • The content of conductive additive is optimized by the percolation theory. • LFPC-2 cathode containing polyaniline possesses the excellent performance. - Abstract: Comprehensive methods were utilized to improve the electrochemical performances of LiFe{sub 0.94}Mg{sub 0.03}Cu{sub 0.03}PO{sub 4}/C (LFPC) composite cathode. Experimental results show that LFPC-2, prepared by adding glucose in two steps, possesses the effective incorporated of doping ions and well-distributed pyrolysis carbon. It possesses higher conductivity and discharge capacity. The percolation theory analysis shows that the conductivity of LFPC-2 cathode film reaches its maximum value at the mass content of 15 wt.%. The replacement of acetylene black with polyaniline can greatly improve the electrochemical performances of LFPC-2 cathode. Its discharge capacity is 85.3 mAh g{sup −1} and its potential platform is as high as 3.2 V at the current density of 850 mA g{sup −1} when 15 wt.% polyaniline is used. The cycle performance of LFPC-2 is improved when polyaniline is used as conductive additives. And the change of charge transfer resistance of LFPC-2 cathode containing polyaniline is very small after 24 cycles.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Cystine-knot peptide inhibitors of HTRA1 bind to a cryptic pocket within the active site region

Cystine-knot peptides (CKPs) are naturally occurring peptides that exhibit exceptional chemical and proteolytic stability. We leveraged the CKP carboxypeptidase A1 inhibitor as a scaffold to construct phage-displayed CKP libraries and subsequently screened these collections against HTRA1, a trimeric serine protease implicated in age-related macular degeneration and osteoarthritis. The initial hits were optimized by using affinity maturation strategies to yield highly selective and potent picomolar inhibitors of HTRA1. Crystal structures, coupled with biochemical studies, reveal that the CKPs do not interact in a substrate-like manner but bind to a cryptic pocket at the S1’ site region of HTRA1 and abolish catalysis by stabilizing a non-competent active site conformation. The opening and closing of this cryptic pocket is controlled by the gatekeeper residue V221, and its movement is facilitated by the absence of a constraining disulfide bond that is typically present in trypsin fold serine proteases, thereby explaining the remarkable selectivity of the CKPs. Our findings reveal an intriguing mechanism for modulating the activity of HTRA1, and highlight the utility of CKP-based phage display platforms in uncovering potent and selective inhibitors against challenging therapeutic targets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

HDBind: encoding of molecular structure with hyperdimensional binary representations

Traditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A low-cost centralized HVAC control system solution for energy savings, load shedding, and improved maintenance

University campuses rely on centralized controls for managing and optimizing complex HVAC systems in larger buildings. However, most campuses also have many smaller buildings with packaged HVAC systems controlled by a stand-alone thermostat. Even when these distributed and often overlooked systems have modern programmable thermostats, they cannot be centrally monitored or controlled, and they are typically not programmed adequately. This paper describes the implementation of a low-cost centralized control solution for these systems serving smaller campus buildings, mostly under 5,000 sf and representative of light commercial spaces. Thanks to advances in technology spurred by residential and commercial IoT developments, simple networked thermostat solutions exist that can easily replace original thermostats, and, connect these systems to a web-based portal for monitoring and control. We show that, with small customizations, these platforms can be integrated into facility management workflows. Beyond the energy savings potential from improved scheduling and closer management of these systems, there are significant advantages for maintenance crews since these systems can now be monitored on smart phones or tablets. A grid-responsive load-shedding program has also been implemented for additional cost savings. The networked thermostats can also be connected to additional systems such as economizer controls for improved ventilation management and energy savings. With data from these systems integrated centrally, it can also be used for improved analytics and fault detection. A toolkit has been developed to share the program with other campuses, whether for energy savings, improved management of ventilation, or a more proactive maintenance approach.

Fauchier-Magnan, Nicolas↗
DOE OSTI2017

Development and Implementation of an Automatic Continuous Online Monitoring and Control Platform for Polymerization Reactions to Sharply Boost Energy and Resource Efficiency in Polymer Manufacturing

The project goal was to create an energy saving paradigm shift in how polymers are manufactured in the 21st century. It used Automatic Continuous Online Monitoring of Polymerization reactions (ACOMP) integrated for the first time with automatic active control to create the innovative ‘ACOMP/Control Interface’, or ‘ACOMP/CI’. ACOMP/CI will begin the transformation from old, inefficient processes into highly evolved, energy and resource efficient ones. The ACOMP platform is broadly applicable to many types of reactions and processes throughout the vast polymer industry. The industry provides materials for sectors such as automotive, aerospace, oil recovery, agriculture, paints, resins, adhesives, pharmaceuticals and therapeutic proteins, optics, electronics, lightweight building materials, and many more. The U.S. chemical industry is one of the last major sectors in which the U.S. has top global stature. It consumes 24% of all U.S. manufacturing energy, produces over $800B of product annually, supports 25% of the U.S. GDP and employs over 6 million people. It is also a major source of GHG emissions. Polymers make up approximately 30% of this sector. It is estimated that annually 60 TBtu of energy could be saved and 3 million tons less of GHG emissions produced by optimizing production in the polyolefin manufacturing sector alone. The project scope included first time design and prototyping of an ACOMP/CI, creation of active reaction controllers, and demonstration of control capabilities on ideal, low concentration polymerization reactions. All these elements of the scope were met, including advances and findings not originally anticipated. Extensions to more complex reactions, beyond the reactor capabilities of the current project ACOMP/CI, such as polyolefins and other high pressure/high temperature reactions, are being proposed in Fall 2017 to CESMII, a DoE based NNMI. The initial proposal was for a three year funded project, but this was reduced to a two year project and budget due to funding constraints. Hence, some of the original plans, such as adaptation of the ACOMP/CI to more relevant industrial processes, such as emulsion and dispersion technologies, could not be carried out. A third year of funding was requested at the end of the project, but DoE did not have resources to grant this. The sub-contractor Fluence Analytics (previously Advanced Polymer Monitoring Technologies, Inc) designed, prototyped, and commissioned a working ACOMP/CI by June 2015. The reaction characteristics to be automatically controlled were i) conversion kinetics, ii) molecular weight, iii) copolymer composition, and iv) simultaneous molecular weight and composition. A two pronged control strategy was used. The Tulane/Fluence group took a basic principles approach that did not rely on kinetic models. The LSU group took a more complex, non-linear model-oriented approach involving complete kinetic descriptions of the reaction system Each of these approaches proved successful in their own way. By April 2016 fully automatic control of conversion and weight average molecular weight, Mw, trajectories was achieved using the Tulane/Fluence (TF) basic principles controller. Similar results were obtained by the LSU non-linear model controller by August 2016. The demonstration system was aqueous free radical polymerization of acrylamide, Am. The control variables were temperature and semi-batch feed to the reactor of Am monomer and initiator. A demonstration of active manual conversion control in an industrial process using high solids in inverse emulsion polymerization of Am was achieved. During Summer 2016 the TF controller was used in conjunction with a chain transfer agent, another control variable, to automatically produce multi-modal molecular weight distributions, MWD, in a single reactor. Industrially, multi-modal MWD are produced by mixing products made in separate reactors, requiring significant extra time, energy, and reactor resources. Recognizing the industrial potential a patent on automatic production of multi-modal polymers was filed, and DoE acknowledged. In Fall 2016 the TF team developed a basic principles controller for copolymer composition and demonstrated it on aqueous free radical copolymerization of comonomers Am and styrene sulfonate, SS. TF then fused the Mw and composition controllers to achieve simultaneous control of both Mw and copolymer composition trajectories. Numerous simultaneous trajectories were demonstrated, including a trimodal composition distribution with constant Mw. Meanwhile, the LSU group developed a Kalman filter to improve the results of their automatic Mw and conversion controller and successful tests were made. During the project the TF team developed a means of computing full MWD during polymer synthesis without need for any chromatographic separation, based on model distributions. This means the polymer product is ‘born characterized’ and this can eliminate post-manufacture analytical laboratory quality control. TF filed a joint patent application on this new approach to chromatography-free determination of MWD with acknowledgment to DoE. The Tulane group obtained a 60MHz NMR during the project and recently completed the first work on separating three comonomers, Am, SS, and Na-acrylate, with a first demonstration of terpolymer composition control with the TF basic principles controller. Widespread dissemination of ACOMP/CI in the polymer manufacturing sector will bolster DoE goals of energy efficiency and reduced GHG emissions: The ability to monitor and actively control polymerization reactions will lead to more efficient use of energy and non-renewable resources, plant and labor time, increase the safety of manufacturing personnel, and will enhance product quality and lead to feasibility of manufacturing of polymers currently too complex for industrial scale production, while leading to less GHG emissions per kilo of product, and allowing for increased U.S. competitiveness in this enormous manufacturing sector. When ACOMP/CI is expanded to the polyolefin industry it is estimated that 60 TeraBTU/year of energy can be saved. Much of this saving is anticipated to come from optimized control of grade changeovers in steady state reactors and maintenance of steady states. Conclusions: ACOMP’s ability to provide continuous realtime data streams of measured polymer and reaction characteristics made it possible, for the first time, to directly and automatically control free radical polymerization reactions. An industrial client of Fluence Analytics has requisitioned the first ACOMP/CI which uses the TF basic principles controller. This sets the stage for FA to add control features to the ACOMP systems it has begun to install on the industrial scale beginning in 2014. Recommendations: This successful project was mainly limited to ideal polymerizations not of an industrial sort. The most energy intensive portion of polymer manufacturing is polyolefins. Adoption of ACOMP/CI to this enormous industrial sector faces the enormous challenges of high temperature, high pressure continuous sampling and high temperature sensor operation to obtain the continuous data needed for direct reaction control. The project team has a strategy for achieving this ambitious goal and will present it in Fall 2017 as a proposal to CESMII/DoE. It is recommended that this upcoming proposal be funded in order to make full use of the achievements of this just ended DoE project as the next step towards making polyolefin ACOMP/CI an energy saving reality. It is projected that ACOMP/CI can have its first polyolefin testbed demonstrations within two years of beginning the proposed project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

THF Co-Solvent Biomass Fractionation to Catalytic Fuel Precursors with High Yields (Final Report)

The overall goal of this project is to utilize the Co-Solvent Enhanced Lignocellulosic Fractionation (CELF) as a platform technology to realize transformation of real biomass feedstocks to fungible transportation fuels. CELF is a solvolysis method that seeks to break the cost barrier for biomass deconstruction so that high yield liquid intermediates from both sugars and lignin can be recovered for their subsequent catalytic conversion to fungible transportation fuels. It is important for the DOE EERE mission to focus on supporting platform technologies that enable greater end-to-end utilization of biomass whereby the co-development of both process and catalyst is streamlined. CELF is one of the most competitive platform technologies that uses multifunctional co-solvents THF and water to provide the cleanest intermediate product streams that can allow advanced catalysts to be developed on real feedstocks instead of model compounds. For this project, we developed a hybrid catalytic process that first applies CELF homogeneous catalysis to convert solid biomass to a liquid stream containing intermediate fuel precursors (FPs) such as furfural (FF) and 5-hydroxymethylfurfural (5-HMF). Afterwards, a second heterogeneous catalytic step was then applied to selectively hydrodeoxygenate (HDO) the FPs produced from biomass into gasoline blendstocks methylfuran (MF) and dimethylfuran (DMF). Concurrently, capitalizing on CELF’s efficient lignin extraction capabilities, we further explored production C9+ cyclic hydrocarbons from biomass lignin to improve overall process economics. For the first step, the CELF process was optimized to achieve yields of >95% for FF and >60% for HMF from the xylan and glucan fractions in poplar wood, respectively. For the second step, we created a patent-pending bifunctional Cu-Ni solid catalyst that is tolerant to real product streams from the CELF process and simultaneously produce MF and DMF blendstocks at >80% yields. To provide more market versatility, we further explored the production of 2-pentanol from MF and have achieved >70% yields. During this project’s three year period, we also developed a dynamically informed technoeconomic computer model that helped to guide key project decisions that allowed us to reach a final biofuel selling price (MFSP) of <$\$$3/GGE (gallon gasoline equivalent) and total biomass carbon utilization of 60%. We also determined that the MFSP in our model could be further reduced to below $\$$2/GGE if the value of the lignin-derived aromatic hydrocarbon fuels could be sold at diesel-range prices (>$\$$500/tonne). Our process model was designed to consume 288 tonnes per day of poplar wood chips with minor consumption of hydrogen to produce a range of fuel blendstocks suitable for use in the transportation sector. The project was successful in meeting DOE EERE program criteria for application of an all-catalytic biofuels process to achieve a biofuel selling price target of <$\$$3/GGE as well as to achieve a 60% reduction in greenhouse gas emissions (GHG) compared to conventional fossil petroleum processes. At project completion, the outlined catalytic process reported total utilization of up to 60% of the carbon found in biomass, rivaling more mature biological cellulosic ethanol technologies at a fraction of the fixed and operating costs. This project also resulted in 7 peer-reviewed publications, 2 provisional inventions, and 1 patent pending. NOTE: This public version of the document contains redacted text, tables, and figures as indicated within the document.

09 BIOMASS FUELS↗
DOE OSTI2020

Runtime power allocation approach for GAMESS hybrid CPU‐GPU implementation

Summary To improve power consumption of applications at the runtime, modern processors provide frequency scaling capabilities, which along with workload optimization, are also available on GPU accelerators. In this work, a runtime strategy is proposed to distribute a given power allocation among the host components and the GPU according to the current application performance and power usage, such that GPU execution is prioritized over CPU for power allocation to maximize application performance. Next, the strategy is tailored to an application, a quantum‐chemistry package GAMESS for ab initio electronic structure calculations. Specifically, GAMESS hybrid CPU–GPU implementation as provided in the Libcchem library is considered. Experiments, performed on a 28‐core node with a Kepler GPU, resulted in performance gains of up to 50% under the proposed strategy and the largest power allocation considered here as compared with the scenario when this allocation was equally distributed among the computing‐platform components.

Sundriyal, Vaibhav↗
DOE OSTI2022

Quantum Phases of Transition Metal Dichalcogenide Moiré Systems

Moiré systems provide a rich platform for studies of strong correlation physics. Recent experiments on heterobilayer transition metal dichalcogenide Moiré systems are exciting in that they manifest a relatively simple model system of an extended Hubbard model on a triangular lattice. Inspired by the prospect of the hetero-transition metal dichalcogenide Moiré system’s potential as a solid-state-based quantum simulator, we explore the extended Hubbard model on the triangular lattice using the density matrix renormalization group. Specifically, we explore the two-dimensional phase space spanned by the key tuning parameters in the extended Hubbard model, namely, the kinetic energy strength and the further-range Coulomb interaction strengths. We find competition between Fermi fluid, chiral spin liquid, spin density wave, and charge order. In particular, our finding of the optimal further-range interaction for the chiral correlation presents a tantalizing possibility.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Accelerating Scientific Workflows on HPC Platforms with In Situ Processing

Scientific workflows drive most modern large-scale science breakthroughs by allowing scientists to define their computations as a set of jobs executed in a given order based on their data dependencies. Workflow management systems (WMSs) have become key to automating scientific workflows-executing computational jobs and orchestrating data transfers between those jobs running on complex high-performance computing (HPC) platforms. Traditionally, WMSs use files to communicate between jobs: a job writes out files that are read by other jobs. However, HPC machines face a growing gap between their storage and compute capabilities. To address that concern, the scientific community has adopted a new approach called in situ, which bypasses costly parallel filesystem I/O operations with faster in-memory or in-network communications. When using in situ approaches, communication and computations can be interleaved. In this work, we leverage the Decaf in situ dataflow framework to accelerate task-based scientific workflows managed by the Pegasus WMS, by replacing file communications with faster MPI messaging. We propose a new execution engine that uses Decaf to manage communications within a sub-workflow (i.e., set of jobs) to optimize inter-job communications. We consider two workflows in this study: (i) a synthetic workflow that benchmarks and compares file- and MPI-based communication; and (ii) a realistic bioinformatics workflow that computes mu-tational overlaps in the human genome. Experiments show that in situ communication can improve the bioinformatics workflow execution time by 22% to 30% compared with file communication. Our results motivate further opportunities and challenges for bridging traditional WMSs with in situ frameworks.

Decaf↗
DOE OSTI2026

Evolution_and_engineering_of_pathways_for_aromatic_O-demethylation_in_Pseudomonas_putida_KT2440

Biological conversion of lignin from biomass offers a promising strategy for sustainable production of fuels and chemicals. However,aromatic compoundsderived from lignin commonly contain methoxy groups, andO-demethylation of these substrates is often a rate-limiting reaction that influences catabolic efficiency. Severalenzymefamilies catalyze aromaticO-demethylation, but they are rarely comparedin vivoto determine an optimal biocatalytic strategy. Here, two pathways for aromaticO-demethylation were compared inPseudomonas putidaKT2440. The native Rieske non-heme ironmonooxygenase(VanAB) and, separately, a heterologous tetrahydrofolate-dependentdemethylase(LigM) were constitutively expressed inP. putida, and the strains were optimized via adaptive laboratory evolution (ALE) withvanillateas a model substrate. All evolved strains displayed improved growth phenotypes, with the evolved strains harboring the native VanAB pathway exhibiting growth rates ∼1.8x faster than those harboring the heterologous LigM pathway.Enzyme kineticsandtranscriptomicsstudies investigated the contribution of selected mutations toward enhanced utilization of vanillate. The VanAB-overexpressing strains contained the most impactful mutations, including those in VanB, thereductasefor vanillateO-demethylase, PP_3494, a global regulator of vanillate catabolism, andfghA, involved in formaldehydedetoxification. These three mutations were combined into a single strain, which exhibited approximately 5x faster vanillate consumption than the wild-type strain in the first 8 h of cultivation. Overall, this study illuminates the details of vanillate catabolism in the context of two distinct enzymatic mechanisms,yielding a platform strain for efficientO-demethylation of lignin-related aromatic compounds to value-added products. This DOI contains supplementary material associated with the published manuscript.

CBI↗
DOE OSTI2026

Magnetic flux distribution, quasiparticle spectroscopy, and quality factors in Nb films for superconducting qubits

Niobium is a practical material platform for superconducting microwave circuits; however, device-level performance can vary significantly depending on film growth and processing conditions. We compare three epitaxial Nb films grown on $c-$plane sapphire substrates under nominally identical conditions, except for the deposition temperature. To correlate internal quality factors, $Q_{\mathrm {i}}$, with material properties, we combine magneto-optical imaging of magnetic flux distribution with quasiparticle spectroscopy via measurements of the London penetration depth, $λ(T)$. In the low-$Q_{\mathrm i}$ film, there is a lesser ability to screen the magnetic field and an irregular temperature variation of $λ(T)$, implying the existence of localized in-gap states. High $Q_{\mathrm i}$ films show the opposite trend. We conclude that our measurements provide an efficient method for characterizing and optimizing superconducting films for quantum informatics applications.

Datta, Amlan [Ames Lab; Iowa State U.]↗
DOE OSTI2024

Ultrapotent influenza hemagglutinin fusion inhibitors developed through SuFEx-enabled high-throughput medicinal chemistry

Seasonal and pandemic-associated influenza strains cause highly contagious viral respiratory infections that can lead to severe illness and excess mortality. Here, we report on the optimization of our small-molecule inhibitor F0045(S) targeting the influenza hemagglutinin (HA) stem with our Sulfur-Fluoride Exchange (SuFEx) click chemistry–based high-throughput medicinal chemistry (HTMC) strategy. A combination of SuFEx- and amide-based lead molecule diversification and structure-guided design led to identification and validation of ultrapotent influenza fusion inhibitors with subnanomolar EC 50 cellular antiviral activity against several influenza A group 1 strains. X-ray structures of six of these compounds with HA indicate that the appended moieties occupy additional pockets on the HA surface and increase the binding interaction, where the accumulation of several polar interactions also contributes to the improved affinity. The compounds here represent the most potent HA small-molecule inhibitors to date. Our divergent HTMC platform is therefore a powerful, rapid, and cost-effective approach to develop bioactive chemical probes and drug-like candidates against viral targets.

Science & Technology - Other Topics↗