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At least 1,189 records · Page 66

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DOE OSTI2021

Correlated oxide Dirac semimetal in the extreme quantum limit

Quantum materials (QMs) with strong correlation and nontrivial topology are indispensable to next-generation information and computing technologies. Exploitation of topological band structure is an ideal starting point to realize correlated topological QMs. Here, we report that strain-induced symmetry modification in correlated oxide SrNbO 3 thin films creates an emerging topological band structure. Dirac electrons in strained SrNbO 3 films reveal ultrahigh mobility (μ max ≈ 100,000 cm 2 /Vs), exceptionally small effective mass (m* ~ 0.04m e ), and nonzero Berry phase. Strained SrNbO 3 films reach the extreme quantum limit, exhibiting a sign of fractional occupation of Landau levels and giant mass enhancement. Our results suggest that symmetry-modified SrNbO 3 is a rare example of correlated oxide Dirac semimetals, in which strong correlation of Dirac electrons leads to the realization of a novel correlated topological QM.

36 MATERIALS SCIENCE↗

DOE OSTI2022

The molecular impact of life in an indoor environment

The chemistry of indoor surfaces and the role of microbes in shaping and responding to that chemistry are largely unexplored. We found that, over 1 month, people’s presence and activities profoundly reshaped the chemistry of a house. Molecules associated with eating/cooking, bathroom use, and personal care were found throughout the entire house, while molecules associated with medications, outdoor biocides, and microbially derived compounds were distributed in a location-dependent manner. The house and its microbial occupants, in turn, also introduced chemical transformations such as oxidation and transformations of foodborne molecules. The awareness of and the ability to observe the molecular changes introduced by people should influence future building designs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

(Mg,Mn,Fe,Co,Ni)O: A rocksalt high-entropy oxide containing divalent Mn and Fe

High-entropy oxides (HEOs) have aroused growing interest due to fundamental questions relating to their structure formation, phase stability, and the interplay between configurational disorder and physical and chemical properties. Introducing Fe(ιι) and Mn(ιι) into a rocksalt HEO is considered challenging, as theoretical analysis suggests that they are unstable in this structure under ambient conditions. Here, we develop a bottom-up method for synthesizing Mn- and Fe-containing rocksalt HEO (FeO-HEO). We present a comprehensive investigation of its crystal structure and the random cation-site occupancy. We show the improved structural robustness of this FeO-HEO and verify the viability of an oxygen sublattice as a buffer layer. Compositional analysis reveals the valence and spin state of the iron species. We further report the antiferromagnetic order of this FeO-HEO below the transition temperature ~218 K and predict the conditions of phase stability of Mn- and Fe-containing HEOs. Our results provide fresh insights into the design and property tailoring of emerging classes of HEOs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Architecture and functional regulation of a plant PSII-LHCII megacomplex

Photosystem II (PSII) splits water in oxygenic photosynthesis on Earth. The structure and function of the C 4 S 4 M 2 -type PSII-LHCII (light-harvesting complex II) megacomplexes from the wild-type and PsbR-deletion mutant plants are studied through electron microscopy (EM), structural mass spectrometry, and ultrafast fluorescence spectroscopy [time-resolved fluorescence (TRF)]. The cryo-EM structure of a type I C 4 S 4 M 2 megacomplex demonstrates that the three domains of PsbR bind to the stromal side of D1, D2, and CP43; associate with the single transmembrane helix of the redox active Cyt b 559 ; and stabilize the luminal extrinsic PsbP, respectively. This megacomplex, with PsbR and PsbY centered around the narrow interface between two dimeric PSII cores, provides the supramolecular structural basis that regulates the plastoquinone occupancy in Q B site, excitation energy transfer, and oxygen evolution. PSII-LHCII megacomplexes (types I and II) and LHC aggregation levels in Arabidopsis psbR mutant were also interrogated and compared to wild-type plants through EM and picosecond TRF.

Science & Technology - Other Topics↗

DOE OSTI2026

Diffraction-quality, ultraflexible protein single crystals engineered with DNA

DNA-functionalized colloidal nanoparticles assemble through flexible, nanoscale DNA hybridization interactions that limit atomic-level structural order. Here, we report a valence-centric strategy that enables DNA-bonded, protein single crystals with unconventional mechanical properties. An octameric enzyme, glutarate L-2-hydroxylase, was site- and number-selectively conjugated with eight self-complementary single-stranded DNA, yielding octavalent molecular bonds. The resulting conjugate assembled into the designed body-centered tetragonal crystals that diffracted to 1.42- to 2.61-angstrom resolution, with contacts mediated by B-form DNA helices spanning 17 to 25 angstroms. Increasing oligonucleotide length induces anisotropic lattice expansion while preserving atomic periodicity, even with partial DNA occupancy. Mechanistic studies suggest that the dynamic motion of unhybridized DNA facilitates crystallization, analogous to fluctuating electron clouds in atomic bonding. Compared with native protein crystals, DNA-hybridized crystals are 23-fold softer. These results challenge the assumption that flexibility is incompatible with structural order and establish a programmable framework for biomolecular crystallization and nanomaterials engineering with atomic precision.

Han, Zhenyu [Department of Chemistry, Northwestern↗

DOE OSTI2022

Reduction of a Heme Cofactor Initiates N -Nitroglycine Degradation by NnlA

Linear nitramines are potentially carcinogenic environmental contaminants. The NnlA enzyme from Variovorax sp. strain JS1663 degrades the nitramine N-nitroglycine (NNG)—a natural product produced by some bacteria—to glyoxylate and nitrite (NO 2 –). Ammonium (NH 4 +) was predicted as the third product of this reaction. A source of nonheme Fe II was shown to be required for initiation of NnlA activity. However, the role of this Fe II for NnlA activity was unclear. This work reveals that NnlA contains a b-type heme cofactor. Reduction of this heme—either by a nonheme iron source or dithionite—is required to initiate NnlA activity. Therefore, Fe II is not an essential substrate for holoenzyme activity. Our data show that reduced NnlA (Fe II -NnlA) catalyzes at least 100 turnovers and does not require O 2 . Finally, NH 4 + was verified as the third product, accounting for the complete nitrogen mass balance. Size exclusion chromatography showed that NnlA is a dimer in solution. Additionally, Fe II -NnlA is oxidized by O 2 and NO 2 – and stably binds carbon monoxide (CO) and nitric oxide (NO). These are characteristics shared with heme-binding PAS domains. Furthermore, a structural homology model of NnlA was generated using the PAS domain from Pseudomonas aeruginosa Aer2 as a template. The structural homology model suggested His 73 is the axial ligand of the NnlA heme. Site-directed mutagenesis of His 73 to alanine decreased the heme occupancy of NnlA and eliminated NNG activity, validating the homology model. We conclude that NnlA forms a homodimeric heme-binding PAS domain protein that requires reduction for initiation of the activity.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Elevated Expression of a Functional Suf Pathway in Escherichia coli BL21(DE3) Enhances Recombinant Production of an Iron-Sulfur Cluster-Containing Protein

Structural and spectroscopic analysis of iron-sulfur [Fe-S] cluster-containing proteins is often limited by the occupancy and yield of recombinantly produced proteins. In this work, we report that Escherichia coli BL21(DE3), a strain routinely used to overproduce [Fe-S] cluster-containing proteins, has a nonfunctional Suf pathway, one of two E. coli [Fe-S] cluster biogenesis pathways. We confirmed that BL21(DE3) and commercially available derivatives carry a deletion that results in an in-frame fusion of sufA and sufB genes within the sufABCDSE operon. We show that this fusion protein accumulates in cells but is inactive in [Fe-S] cluster biogenesis. Restoration of an intact Suf pathway combined with enhanced suf operon expression led to a remarkable (~3-fold) increase in the production of the [4Fe-4S] cluster-containing BchL protein, a key component of the dark-operative protochlorophyllide oxidoreductase complex. These results show that this engineered “SufFeScient” derivative of BL21(DE3) is suitable for enhanced large-scale synthesis of an [Fe-S] cluster-containing protein.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Detection of Airborne Influenza A and SARS-CoV-2 Virus Shedding following Ocular Inoculation of Ferrets

Despite reports of confirmed human infection following ocular exposure with both influenza A virus (IAV) and SARS-CoV-2, the dynamics of virus spread throughout oculonasal tissues and the relative capacity of virus transmission following ocular inoculation remain poorly understood. Furthermore, the impact of exposure route on subsequent release of airborne viral particles into the air has not been examined previously. To assess this, ferrets were inoculated by the ocular route with A(H1N1)pdm09 and A(H7N9) IAVs and two SARS-CoV-2 (early pandemic Washington/1 and Delta variant) viruses. Virus replication was assessed in both respiratory and ocular specimens, and transmission was evaluated in direct contact or respiratory droplet settings. Viral RNA in aerosols shed by inoculated ferrets was quantified with a two-stage cyclone aerosol sampler (National Institute for Occupational Safety and Health [NIOSH]). All IAV and SARS-CoV-2 viruses mounted a productive and transmissible infection in ferrets following ocular inoculation, with peak viral titers and release of virus-laden aerosols from ferrets indistinguishable from those from ferrets inoculated by previously characterized intranasal inoculation methods. Viral RNA was detected in ferret conjunctival washes from all viruses examined, though infectious virus in this specimen was recovered only following IAV inoculation. Low-dose ocular-only aerosol exposure or inhalation aerosol exposure of ferrets to IAV similarly led to productive infection of ferrets and shedding of aerosolized virus. Viral evolution during infection was comparable between all inoculation routes examined. Furthermore, these data support that both IAV and SARS-CoV-2 can establish a high-titer mammalian infection following ocular exposure that is associated with rapid detection of virus-laden aerosols shed by inoculated animals.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Convergence of Weak-SINDy Surrogate Models

In this paper, we give an in-depth error analysis for surrogate models generated by a variant of the Sparse Identification of Nonlinear Dynamics (SINDy) method. We start with an overview of a variety of nonlinear system identification techniques, namely SINDy, weak-SINDy, and the occupation kernel method. Under the assumption that the dynamics are a finite linear combination of a set of basis functions, these methods establish a linear system to recover coefficients. We illuminate the structural similarities between these techniques and establish a projection property for the weak-SINDy technique. Following the overview, we analyze the error of surrogate models generated by a simplified version of weak-SINDy. In particular, under the assumption of boundedness of a composition operator given by the solution, we show that (i) the surrogate dynamics converges towards the true dynamics and (ii) the solution of the surrogate model is reasonably close to the true solution. Finally, as an application, we discuss the use of a combination of weak-SINDy surrogate modeling and proper orthogonal decomposition (POD) to build a surrogate model for partial differential equations (PDEs).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Single-neutron adding on $^{34}$S

For this work, single-neutron adding data was collected in order to determine the distribution of the single-neutron strength of the 0ƒ 7/2 , 1p 3/2 , 1p 1/2 and 0ƒ 5/2 orbitals outside of Z = 16, N = 18, 34 S. The 34 S(d,p) 35 S reaction has been measured at 8 MeV/u to investigate cross sections to excited states in 35 S. Outgoing proton yields and momenta were analyzed by the Super-Enge Split-Pole Spectrograph in conjunction with the CeBrA demonstrator located at the John D. Fox Laboratory at Florida State University. Angular distributions were compared with Distorted Wave Born Approximation calculations in order to extract single-neutron spectroscopic overlaps. Spectroscopic overlaps and strengths were determined for states in S up through 6 MeV in excitation energy. Each orbital was observed to have fragmented strength where a single level carried the majority. The single-neutron centroids of the 0ƒ 7/2 , 1p 3/2 , 1p 1/2 and 0ƒ 5/2 orbitals were determined to be 2360$^{+90}_{-40}$ keV, 3280$^{+80}_{-50}$ keV, 4780$^{+60}_{-40}$ keV, and ≳7500 keV, respectively. A previous discrepancy in the literature with respect to the distribution of the neutron 1p 1/2 strength was resolved. The integration of the normalized spectroscopic strengths, up to 5.1 MeV in excitation energy, revealed fully-vacant occupancies for the 0ƒ 7/2 , 1p 3/2 , and 1p 1/2 orbitals, as expected. The spacing in the single-neutron energies highlighted a reduction in the traditional N = 28 shell-gap, relative to both the 1p spin-orbit energy difference (N = 32) and the lower limit on the N = 34 shell spacing.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

The role of the Heisenberg principle in constrained molecular dynamics model

In this study, we implement the Heisenberg principle into the Constrained Molecular Dynamics model with a similar approach to the Pauli principle using the one-body occupation probability $\bar{f}_i$. Results of the modified and the original model with comparisons to data are given. The binding energies and the radii of light nuclei obtained with the modified model are more consistent with the experimental data than the original one. The collision term and the density distribution are tested through a comparison to p+ 12 C elastic scattering data. Some simulations for fragmentation and superheavy nuclei production are also discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

Dataset: Inferring Thermal Comfort using Body Shape Information Utilizing Depth Sensors

Thermal comfort is very important for well-being and productivity of building occupants. It has been shown that body shape is a useful feature to determine thermal comfort of individuals [2]. It is because, the heat dissipation rate of individuals depends on the body surface area. As a result, a tall and skinny person can tolerate higher room temperature than a rounded body shape person [5]. In order to test this hypothesis, we performed a year-long experiment in 2017, where we recruited 77 participants and put each of them in a thermally controlled conference room in CMU for 3 hours and recorded their subjective responses regarding thermal comfort at different temperature ranging from 60°F to 80°F. In addition, we collected depth data of individuals using a vertically mounted Microsoft Kinect for XBOX One at the entrance of the conference room to capture their body shape. We also collected biometric features (e.g., Galvanic Skin Response (GSR), skin temperature) using a Microsoft Health Band worn by the subjects. The resulting dataset provides rich information regarding how different features can be used to infer thermal comfort of the individuals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Gnu-RL: A Precocial Reinforcement Learning Solution for Building HVAC Control Using a Differentiable MPC Policy

Reinforcement learning (RL) was first demonstrated to be a feasible approach to controlling heating, ventilation, and air conditioning (HVAC) systems more than a decade ago. However, there has been limited progress towards a practical and scalable RL solution for HVAC control. While one can train an RL agent in simulation, it is not cost-effective to create a model for each thermal zone or building. Likewise, existing RL agents generally take a long time to learn and are opaque to expert interrogation, making them unattractive for real-world deployment. To tackle these challenges, we propose Gnu-RL: a novel approach that enables practical deployment of RL for HVAC control and re- quires no prior information other than historical data from exist- ing HVAC controllers. To achieve this, Gnu-RL adopts a recently- developed Differentiable Model Predictive Control (MPC) policy, which encodes domain knowledge on planning and system dynam- ics, making it both sample-efficient and interpretable. Prior to any interaction with the environment, a Gnu-RL agent is pre-trained on historical data using imitation learning, which enables it to match the behavior of the existing controller. Once it is put in charge of controlling the environment, the agent continues to improve its policy end-to-end, using a policy gradient algorithm. We evaluate Gnu-RL on both an EnergyPlus model and a real- world testbed. In both experiments, our agents were directly de- ployed in the environment after offline pre-training on expert demonstration. In the simulation experiment, our approach saved 6.6% energy compared to the best published RL result for the same environment, while maintaining a higher level of occupant comfort. Next, Gnu-RL was deployed to control the HVAC of a real-world conference room for a three-week period. Our results show that Gnu-RL saved 16.7% of cooling demand compared to the existing controller and tracked temperature set-point better.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Potential Demand Reduction from Buildings in a Simulated Utility

The availability of large-area sensing, scalable algorithms, and high-performance computing has enabled the possibility of urban-scale building energy modeling using new methods not limited to the scalability of manual building energy model creation or retrieval of county-by-county tax assessor’s data. Automatic Building detection and Energy Model creation (AutoBEM) has created 178,368 building energy models for the Electric Power Board (EPB) of Chattanooga, TN, and compared simulation performance to 15-minute data from each building. These models leverage several data sources (e.g. imagery, GIS, utility), software tools to extract building properties (e.g. footprint, height, façade type, window-to-wall ratio, occupancy, building type), simulate at scale on two of the world’s #1 fastest supercomputers, and provide web-based visual analytics.Grid-interactive efficient buildings offer the potential to reduce utility and rate-payer energy costs during each calendar month’s hour of critical generation – when the least efficient, most costly, and often dirtiest generation assets must be brought online. In this paper, EnergyPlus is used to simulate over 150,000 buildings to assess the technical potential of utility-controlled smart thermostats. This is analyzed under a couple scenarios leveraging buildings as thermal batteries via pre-conditioning to coast through peak hours. Results are provided in box and whisker plots assessing the distribution of peak demand reduction at the utility-scale along with breakouts of energy and demand savings by building type and vintage.

Bass, Brett↗

DOE OSTI2020

Activity Characterization for Modeling Behavioral-driven Human Mobility in Platial Networks

The population is increasingly becoming tractable as more and more people carry handheld devices as part of their everyday activities. Recent studies have shown that handheld devices' generated traffic share is now more than 50% of total global online traffic. This has created an unprecedented opportunity for modeling human mobility behavior. For example, aggregate check-ins and dwell time can reveal building level occupancies. However, there are clear limits to accurate modeling (e.g. reproducible, repeatable, and realistic), unless we decipher the underlying reason causing typical mobility patterns. We know that human behavior is a reflection of a set of activities, such as going to the gym or work, and which can be seen as a catalyst for humans to move from one location to another. This work envisions the use of activity characterization for modeling human mobility by introducing a context that maps activities to certain mobility patterns. In the end, we highlight the efficacy of the proposed approach by analyzing the impact of public policies surrounding stay at home order on human mobility.

Thakur, Gautam Malviya↗

DOE OSTI2021

Generalizable coordination of large multiscale workflows: challenges and learnings at scale

The advancement of machine learning techniques and the heterogeneous architectures of most current supercomputers are propelling the demand for large multiscale simulations that can automatically and autonomously couple diverse components and map them to relevant resources to solve complex problems at multiple scales. Nevertheless, despite the recent progress in workflow technologies, current capabilities are limited to coupling two scales. In the first-ever demonstration of using three scales of resolution, we present a scalable and generalizable framework that couples pairs of models using machine learning and in situ feedback. We expand upon the massively parallel Multiscale Machine-Learned Modeling Infrastructure (MuMMI), a recent, award-winning workflow, and generalize the framework beyond its original design. We discuss the challenges and learnings in executing a massive multiscale simulation campaign that utilized over 600,000 node hours on Summit and achieved more than 98% GPU occupancy for more than 83% of the time. We present innovations to enable several orders of magnitude scaling, including simultaneously coordinating 24,000 jobs, and managing several TBs of new data per day and over a billion files in total. Finally, we describe the generalizability of our framework and, with an upcoming open-source release, discuss how the presented framework may be used for new applications.

Bhatia, Harsh↗

DOE OSTI2021

Learning-based framework for sensor fault-tolerant building HVAC control with model-assisted learning

As people spend up to 87% of their time indoors, intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings are essential for maintaining occupant comfort and reducing energy consumption. These HVAC systems in smart buildings rely 'on real-time sensor readings, which in practice often suffer from various faults and could also be vulnerable to malicious attacks. Such faulty sensor inputs may lead to the violation of indoor environment requirements (e.g., temperature, humidity, etc.) and the increase of energy consumption. While many model-based approaches have been proposed in the literature for building HVAC control, it is costly to develop accurate physical models for ensuring their performance and even more challenging to address the impact of sensor faults. In this work, we present a novel learning-based framework for sensor fault-tolerant HVAC control, which includes three deep learning based components for 1) generating temperature proposals with the consideration of possible sensor faults, 2) selecting one of the proposals based on the assessment of their accuracy, and 3) applying reinforcement learning with the selected temperature proposal. Moreover, to address the challenge of training data insufficiency in building-related tasks, we propose a model-assisted learning method leveraging an abstract model of building physical dynamics. Through extensive experiments, we demonstrate that the proposed fault-tolerant HVAC control framework can significantly reduce building temperature violations under a variety of sensor fault patterns while maintaining energy efficiency.

Xu, Shichao↗

DOE OSTI2023

Revisiting Temporal Blocking Stencil Optimizations

Iterative stencils are used widely across the spectrum of High Performance Computing (HPC) applications. Many efforts have been put into optimizing stencil GPU kernels, given the prevalence of GPU-accelerated supercomputers. To improve the data locality, temporal blocking is an optimization that combines a batch of time steps to process them together. Under the observation that GPUs are evolving to resemble CPUs in some aspects, we revisit temporal blocking optimizations for GPUs. We explore how temporal blocking schemes can be adapted to the new features in the recent Nvidia GPUs, including large scratchpad memory, hardware prefetching, and device-wide synchronization. We propose a novel temporal blocking method, EBISU, which champions low device occupancy to drive aggressive deep temporal blocking on large tiles that are executed tile-by-tile. We compare EBISU with state-of-the-art temporal blocking libraries: STENCILGEN and AN5D. We also compare with state-of-the-art stencil auto-tuning tools that are equipped with temporal blocking optimizations: ARTEMIS and DRSTENCIL. Over a wide range of stencil benchmarks, EBISU achieves speedups up to 2.53x and a geometric mean speedup of 1.49x over the best state-of-the-art performance in each stencil benchmark.

Zhang, Lingqi↗