SEARCH · Search NASA
Results for “Love”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
A Stabilizer Framework for the Contextual Subspace Variational Quantum Eigensolver and the Noncontextual Projection Ansatz
Quantum chemistry is a promising application for noisy intermediate-scale quantum (NISQ) devices. However, quantum computers have thus far not succeeded in providing solutions to problems of real scientific significance, with algorithmic advances being necessary to fully utilize even the modest NISQ machines available today. We discuss a method of ground state energy estimation predicated on a partitioning of the molecular Hamiltonian into two parts: one that is noncontextual and can be solved classically, supplemented by a contextual component that yields quantum corrections obtained via a Variational Quantum Eigensolver (VQE) routine. This approach has been termed Contextual Subspace VQE (CS-VQE); however, there are obstacles to overcome before it can be deployed on NISQ devices. The problem we address here is that of the ansatz, a parametrized quantum state over which we optimize during VQE; it is not initially clear how a splitting of the Hamiltonian should be reflected in the CS-VQE ansätze. We propose a “noncontextual projection” approach that is illuminated by a reformulation of CS-VQE in the stabilizer formalism. This defines an ansatz restriction from the full electronic structure problem to the contextual subspace and facilitates an implementation of CS-VQE that may be deployed on NISQ devices. We validate the noncontextual projection ansatz using a quantum simulator and demonstrate chemically precise ground state energy calculations for a suite of small molecules at a significant reduction in the required qubit count and circuit depth.
Lithium Plating Characteristics in High Areal Capacity Li-Ion Battery Electrodes
Li-ion battery degradation and safety events are often attributed to undesirable metallic lithium plating. Since their release, Li-ion battery electrodes have been made progressively thicker to provide a higher energy density. However, the propensity for plating in these thicker pairings is not well understood. Herein, we combine an experimental plating-prone condition with robust mesoscale modeling to examine electrode pairings with capacities ranging from 2.5 to 6 mAh/cm 2 and negative to positive (N/P) electrode areal capacity ratio from 0.9 to 1.8 without the need for extensive aging tests. Using both experimentation and a mesoscale model, we identify a shift from conventional high state-of-charge (SOC) type plating to high overpotential (OP) type plating as electrode thickness increases. Further, these two plating modes have distinct morphologies, identified by optical microscopy and electrochemical signatures. We demonstrate that under operating conditions where these plating modes converge, a high propensity of plating exists, revealing the importance of predicting and avoiding this overlap for a given electrode pairing. Further, we identify that thicker electrodes, beyond a capacity of 3 mAh/cm 2 or thickness >75 μm, are prone to high OP, limiting negative electrode (NE) utilization and preventing cross-sectional oversizing the NE from mitigating plating. Here, it simply contributes to added mass and volume. The experimental thermal gradient and mesoscale model either combined or independently provide techniques capable of probing performance and safety implications of mild changes to electrode design features.
Polymer, Additives, and Processing Effects on N95 Filter Performance
The current severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) pandemic has highlighted the need for personal protective equipment, specifically filtering facepiece respirators like N95 masks. While it is common knowledge that polypropylene (PP) is the industry standard material for filtration media, trial and error is often required to identify suitable commercial precursors for filtration media production. This work aims to identify differences between several commercial grades of PP and demonstrate the development of N95 filtration media with the intent that the industry partners can pivot and help address N95 shortages. Three commercial grades of high melt flow index PP were melt blown at Oak Ridge National Laboratory and broadly characterized by several methods including differential scanning calorimetry (DSC), X-ray diffraction (XRD), and neutron scattering. Despite the apparent similarities (high melt flow and isotacticity) between PP feedstocks, the application of corona charging and charge enhancing additives improve each material to widely varying degrees. From the analysis performed here, the most differentiating factor appears to be related to crystallization of the polymer and the resulting electret formation. Materials with higher crystallization onset temperatures, slower crystallization rates, and larger number of crystallites form a stronger electret and are more effective at filtration.
Development of Data-Driven Models for Performance Prediction and Chemical Dosing of a Full-Scale Controlled Phosphorus Precipitation Reactor
This study evaluated the use of data-driven models to improve control of a struvite precipitation reactor that removes phosphorus from wastewater while producing a fertilizer product. The researchers developed predictive models for influent orthophosphate concentration, effluent orthophosphate concentration, and phosphorus removal using operational data from a full-scale MagPrex™ reactor at a water resource recovery facility in Denver, Colorado. Model predictions were used to recommend magnesium chloride dosing adjustments needed to achieve a target effluent phosphorus concentration. Several machine learning approaches were tested, with ridge regression providing the best predictions for influent orthophosphate concentration and phosphorus removal, and XGBoost providing the best predictions for effluent orthophosphate concentration. Simulation results indicated that the decision-support approach could correctly identify dosing adjustments in most cases and reduce chemical use. Full-scale implementation achieved lower accuracy due to changing operating conditions and limited historical data in some operating ranges. Here, the results demonstrate the potential of data-driven tools to support phosphorus recovery process control while also identifying practical limitations that affect deployment in full-scale systems.
Numerical Simulations of the Geospace Response to the Arrival of an Idealized Perfect Interplanetary Coronal Mass Ejection
Abstract Previously, Tsurutani and Lakhina (2014, https://doi.org/10.1002/2013GL058825 ) created estimates for a “perfect” interplanetary coronal mass ejection and performed simple calculations for the response of geospace, including . In this study, these estimates are used to drive a coupled magnetohydrodynamic‐ring current‐ionosphere model of geospace to obtain more physically accurate estimates of the geospace response to such an event. The sudden impulse phase is examined and compared to the estimations of Tsurutani and Lakhina (2014, https://doi.org/10.1002/2013GL058825 ). The physics‐based simulation yields similar estimates for Dst rise, magnetopause compression, and equatorial values as the previous study. However, results diverge away from the equator. values in excess of 30 nT/s are found as low as magnetic latitude. Under southward interplanetary magnetic field conditions, magnetopause erosion combines with strong region one Birkeland currents to intensify the response. Values obtained here surpass those found in historically recorded events and set the upper threshold of extreme geomagnetically induced current activity at Earth.
Understanding the structural basis of HIV-1 restriction by the full length double-domain APOBEC3G
APOBEC3G, a member of the double-domain cytidine deaminase (CD) APOBEC, binds RNA to package into virions and restrict HIV-1 through deamination-dependent or deamination-independent inhibition. Mainly due to lack of a full-length double-domain APOBEC structure, it is unknown how CD1/CD2 domains connect and how dimerization/multimerization is linked to RNA binding and virion packaging for HIV-1 restriction. We report rhesus macaque A3G structures that show different inter-domain packing through a short linker and refolding of CD2. The A3G dimer structure has a hydrophobic dimer-interface matching with that of the previously reported CD1 structure. A3G dimerization generates a surface with intensified positive electrostatic potentials (PEP) for RNA binding and dimer stabilization. Unexpectedly, mutating the PEP surface and the hydrophobic interface of A3G does not abolish virion packaging and HIV-1 restriction. The data support a model in which only one RNA-binding mode is critical for virion packaging and restriction of HIV-1 by A3G.
Neuromorphic overparameterisation and few-shot learning in multilayer physical neural networks
Abstract Physical neuromorphic computing, exploiting the complex dynamics of physical systems, has seen rapid advancements in sophistication and performance. Physical reservoir computing, a subset of neuromorphic computing, faces limitations due to its reliance on single systems. This constrains output dimensionality and dynamic range, limiting performance to a narrow range of tasks. Here, we engineer a suite of nanomagnetic array physical reservoirs and interconnect them in parallel and series to create a multilayer neural network architecture. The output of one reservoir is recorded, scaled and virtually fed as input to the next reservoir. This networked approach increases output dimensionality, internal dynamics and computational performance. We demonstrate that a physical neuromorphic system can achieve an overparameterised state, facilitating meta-learning on small training sets and yielding strong performance across a wide range of tasks. Our approach’s efficacy is further demonstrated through few-shot learning, where the system rapidly adapts to new tasks.
Adaptive stretching of representations across brain regions and deep learning model layers
Prefrontal cortex (PFC) is known to modulate the visual system to favor goal-relevant information by accentuating task-relevant stimulus dimensions. Does the brain broadly re-configures itself to optimize performance by stretching visual representations along task-relevant dimensions? We considered a task that required monkeys to selectively attend on a trial-by-trial basis to one of two dimensions (color or motion direction) to make a decision. Although effects were most prominent in frontal areas, representations stretched along task-relevant dimensions in all sites considered: V4, MT, lateral PFC, frontal eye fields (FEF), lateral intraparietal cortex (LIP), and inferotemporal cortex (IT). Spike timing was crucial to this code. A deep learning model was trained on the same visual input and rewards as the monkeys. Despite lacking an explicit selective attention or other control mechanism, by minimizing error during learning, the model’s representations stretched along task-relevant dimensions, indicating that stretching is an adaptive strategy.
Contextual subspace variational quantum eigensolver calculation of the dissociation curve of molecular nitrogen on a superconducting quantum computer
Abstract We present an experimental demonstration of the Contextual Subspace Variational Quantum Eigensolver on superconducting hardware. Calculating the potential energy curve of molecular nitrogen proves challenging for many conventional quantum chemistry techniques, since static correlation dominates in the dissociation limit. Our quantum simulations retain good agreement with the Full Configuration Interaction energy, outperforming all benchmarked single-reference wavefunction techniques in capturing the bond-breaking appropriately. Moreover, our methodology is competitive with multiconfigurational approaches but at a saving of quantum resource, meaning larger active spaces can be treated for a fixed qubit allowance. To achieve this result, we deploy an error mitigation/suppression strategy comprised of Dynamical Decoupling, Measurement-Error Mitigation and Zero-Noise Extrapolation. Circuit parallelization also provides passive noise-averaging and improves the effective shot yield to reduce the measurement overhead. Furthermore, we introduce a modified adaptive ansatz construction algorithm that incorporates hardware awareness into our variational circuits, minimizing the transpilation cost for the target qubit topology.
Toward impact-based monitoring of drought and its cascading hazards
Not provided.
An end-to-end workflow for executing a classically bootstrapped variational quantum algorithm on an academic quantum computer
Academic quantum computing platforms often face unique challenges in executing quantum workloads due to fragmented software environments and limited engineering support. Unlike commercial ecosystems, academic devices typically evolve without full-stack integration in mind, making it difficult to run complex applications—such as variational quantum algorithms (VQA)—reliably and efficiently. Issues such as incompatible software layers and lack of automated job management significantly increase the overhead of theory-experiment collaboration. To address these challenges, we develop a modular, end-to-end workflow that decouples application-layer code from low-level hardware control, automates circuit submission and result collection, and supports fine-grained circuit-level job scheduling and recovery. The architecture employs a dual-end application programming interface (API) design, enabling robust operation across unstable or resource-constrained hardware backends. For practical use, the framework is lightweight and user-friendly, allowing rapid prototyping of full-stack workflows using basic Python tools. We validate this workflow on a high-fidelity trapped-ion quantum computer by demonstrating a variational quantum eigensolver (VQE) experiment with a classically bootstrapped ansatz initialization technique. The system successfully executed over 60,000 circuits across multiple molecular test cases with minimal human intervention, highlighting the framework’s effectiveness in enabling reproducible, resilient quantum experimentation in academic settings.
Pressure-induced inclusion of neon in the crystal structure of a molecular Cu 2 (pacman) complex at 4.67 GPa
Crystals of Cu 2 (pacman) inflate on taking up neon at 46 000 atm through a switch in the ligand conformation.
Selective oxo ligand functionalisation and substitution reactivity in an oxo/catecholate-bridged U IV /U IV Pacman complex
A U IV /U IV “Pacman” complex exhibits facile oxo-ligand functionalisation and substitution reactivity, owing to a bridging catecholate ligand that enhances the μ-oxo nucleophilicity and stabilises the resulting products.
Design, synthesis, and characterization of vinyl-addition polynorbornenes with tunable thermal properties
Unfunctionalized vinyl-addition polynorbornene (VAPNB) possesses many outstanding properties such as high thermal, chemical, and oxidative stability. These features make VAPNB a promising candidate for many engineering applications. However, VAPNB has a small service window between its glass transition temperature (T g ) and decomposition temperature (T d ), and it cannot be readily processed in a melt state. In this work, we demonstrate that the service window of VAPNBs can be tailored through the use of norbornene monomers bearing alkyl, aryl, and aryl ether substituents. The vinyl addition homopolymerization and copolymerization of these functionalized norbornyl-based monomers yielded VAPNBs with high T' g s (>150 °C) and large service windows (T d –T g > 100 °C), which are comparable to other commercial engineering thermoplastics. To further establish the feasibility of melt processing, a functionalized VAPNB material with T g = 209 °C and a service window of 170 °C was successfully extruded and molded into bars. Subsequent characterization of the bars by dynamic mechanical analysis (DMA), nuclear magnetic resonance spectroscopy (NMR), and gel permeation chromatography (GPC) revealed only minor signs of polymer degradation. Furthermore, these studies suggest that substituted VAPNBs could be developed into a new class of engineering thermoplastics that is compatible with workhorse melt processing techniques such as extrusion and injection molding, as well as emerging techniques such as extrusion-based 3D printing.
Direct metal–carbon bonding in symmetric bis(C–H) agostic nickel( i ) complexes
Symmetric Ni( i ) agostic complexes reveal an unusual mode of bonding that is dominated by direct carbon-to-metal charge transfer.
Let’s get our hands dirty: a comprehensive evaluation of DAQDB, key-value store for petascale hot storage
Data acquisition systems are a key component for successful data taking in any experiment. The DAQ is a complex distributed computing system and coordinates all operations, from the data selection stage of interesting events to storage elements. For the High Luminosity upgrade of the Large Hadron Collider, the experiments at CERN need to meet challenging requirements to record data with a much higher occupancy in the detectors. The DAQ system will receive and deliver data with a significantly increased trigger rate, one million events per second, and capacity, terabytes of data per second. An effective way to meet these requirements is to decouple real-time data acquisition from event selection. Data fragments can be temporarily stored in a large distributed key-value store. Fragments belonging to the same event can be then queried on demand, by the data selection processes. Implementing such a model relies on a proper combination of emerging technologies, such as persistent memory, NVMe SSDs, scalable networking, and data structures, as well as high performance, scalable software. In this paper, we present DAQDB (Data Acquisition Database) — an open source implementation of this design that was presented earlier, with an extensive evaluation of this approach, from the single node to the distributed performance. Furthermore, we complement our study with a description of the challenges faced and the lessons learned while integrating DAQDB with the existing software framework of the ATLAS experiment.
Preparation of the Multi-Site Data Processing at the Vera C. Rubin Observatory
The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) Camera is scheduled to start taking data in the summer of 2025. The Data Release Production will run the LSST Science Pipe software at data facilities in the US, France and the UK. The LSST Science Pipeline consists of complex directed acyclic graphs (DAGs) of tasks. Rubin will use the Production and Distributed Analysis (PanDA) workflow and workload management system to orchestrate this complex workflow and the distribution of workloads to the data facilities. When run end-to-end by a team of data production staff, this processing (the Science Pipelines, distributed by the workflow and workload management system) is referred to as a 'campaign'. This paper describes the central services and data facility specific services that support this multi-site data process model, including the service deployment infrastructure, the workload and workflow system, the Campaign Management tools, and connection to Rubin Data Management. This paper will also mention the experience of processing the Rubin Commissioning Camera data. All these are part of the effort to scale up the processing capabilities for the expected very large data volume from the LSST Camera.