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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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This work highlights the potential of lignin-derivable compounds for the development of bio-derivable polysulfones with improved hydrophilicity due to the functionality (methoxy groups) of lignin-aromatics.
The neutron time-of-flight (nToF) diagnostic technique has a lengthy history in Inertial Confinement Fusion (ICF) and High Energy Density (HED) Science experiments. Its initial utility resulted from the simple relationship between the full width half maximum of the fusion peak signal in a distant detector and the burn averaged conditions of an ideal plasma producing the flux [Lehner and Pohl, Z. Phys. 207, 83–104 (1967)]. More recent precision measurements [Gatu-Johnson et al., Phys. Rev. E 94(8), 021202 (2016)] and theoretical studies [Munro, Nucl. Fusion 56, 035001 (2016)] have shown the spectrum to be more subtle and complicated, driving the desire for an absolute calibration of the spectrum to disambiguate plasma dynamics from the conditions producing thermonuclear reactions. In experiments where the neutron production history is not well measured, but the neutron signal is preceded by a concomitant flux of photons, the spectrum can be in situ calibrated using a set of collinear detectors to obtain a true “time-of-flight” measurement. This article presents the motivation and overview of this technique along with estimates of the experimental precision needed to make useful measurements in existing and future nToF systems such as the pulsed power Z-machine located in Albuquerque, NM, at Sandia National Laboratories.
Modeling chemical reactions with quantum chemical methods is challenging when the electronic structure varies significantly throughout the reaction and when electronic excited states are involved. Multireference methods, such as complete active space self-consistent field (CASSCF), can handle these multiconfigurational situations. However, even if the size of the needed active space is affordable, in many cases, the active space does not change consistently from reactant to product, causing discontinuities in the potential energy surface. The localized active space SCF (LASSCF) is a cheaper alternative to CASSCF for strongly correlated systems with weakly correlated fragments. The method is used for the first time to study a chemical reaction, namely the bond dissociation of a mono-, di-, and triphenylsulfonium cation. LASSCF calculations generate smooth potential energy scans more easily than the corresponding, more computationally expensive CASSCF calculations while predicting similar bond dissociation energies. Furthermore, our calculations suggest a homolytic bond cleavage for di- and triphenylsulfonium and a heterolytic pathway for monophenylsulfonium.
Recently, short-range order (SRO) has attracted significant attention, challenging the conventional view of the atomic positions in alloys being random. Furthermore, the presence of SRO has been predicted to have profound effects on the electronic and topological properties of group-IV alloys, offering a different direction in designing group-IV materials for photoelectronic and quantum devices. However, due to the limited understanding of the formation mechanisms, developing effective methods to manipulate SRO in epitaxy is still challenging. To address this, we propose a mechanism for the GeSn alloy, revealing that surface diffusion plays a key role in SRO formation. Building on this mechanism, we show that the distinct surface conditions in MBE and CVD lead to the formation of SRO with enhanced Sn–Sn pairing in MBE-grown samples, while CVD-grown samples remain random alloys. Furthermore, our findings provide an initial understanding of the kinetic process of SRO formation, providing guidance for the design of experiments to manipulate SRO.
The cerium iso-structural phase transition (γ–α) is dominated by f-electron localization changes that results in a magnetic ordering change and a volume collapse. Generally, these physics are difficult to capture with ab initio and first principles methods. However, previous works have shown various methods to be successful in predicting at least some of the physics of the γ to α phase transition. Therefore, here, we perform a broad survey of density functional based methods across three levels of theory and types of functions [GGA (generalized gradient approximation), MetaGGA, and hybrid functionals] and compare the results, focusing on hydrostatic compression across the phase boundary at zero kelvin. For the methods that best reproduce experimental results, we directly probe the predicted mechanisms and frame the results in the Mott/Kondo debate, assessing how the underlying methods and assumptions of different functionals can assess the physical drivers in the phase transition, providing insight into the governing processes of this unique phase transition.
High-valent Fe IV =O species are common intermediates in biological and artificial catalysts. Heme and nonheme S=1 Fe IV =O sites have been synthesized and studied for decades but little quantitative experimental comparison of their electronic structures has been available, due to the lack of direct methods focused on the iron. This study allows a rigorous determination of the electronic structure of a nonheme Fe IV =O center and its comparison to an Fe IV =O heme site using 1s2p resonant inelastic X-ray scattering (RIXS) and Fe L-edge X-ray absorption spectroscopy (XAS). Further, variable temperature magnetic circular dichroism (VT-MCD) of the ligand field transitions, combined with nuclear resonance vibrational spectroscopy of the two S=1 Fe IV =O systems show that the equatorial ligand field decreases from a nonheme to a heme Fe IV =O site. Alternatively, RIXS and Fe L-edge XAS combined with MCD show that the Fe dπ orbitals are unperturbed in the Fe IV =O heme relative to the nonheme site because the strong axial Fe-O bond uncouples the Fe dπ orbitals from the porphyrin π-system. As a consequence, the thermodynamics and kinetics of the H-atom abstraction reactions are actually very similar for heme compound II and nonheme Fe IV =O active sites.
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The prediction of electronic structure for strongly correlated molecules represents a promising application for near-term quantum computers. Significant attention has been paid to ground state wavefunctions, but excited states of molecules are relatively unexplored. In this work, we consider the adaptive, problem-tailored (ADAPT)-variational quantum eigensolver (VQE) algorithm, a single-reference approach for obtaining ground states, and its state-averaged generalization for computing multiple states at once. We demonstrate for both rectangular and linear H4, as well as for BeH2, that this approach, which we call multistate-objective, Ritz-eigenspectral (MORE)-ADAPT-VQE, can make better use of small excitation manifolds than an analogous method based on a single-reference ADAPT-VQE calculation, q-sc-EOM. In particular, MORE-ADAPT-VQE is able to accurately describe both avoided crossings and crossings between states of different symmetries. In addition to more accurate excited state energies, MORE-ADAPT-VQE can recover accurate transition dipole moments in situations where traditional ADAPT-VQE and q-sc-EOM struggle. These improvements suggest a promising direction toward the use of quantum computers for difficult excited state problems.
Here, we present two approaches for “painting” baryonic properties relevant to the Sunyaev-Zel’dovich (SZ) effect—optical depth and Compton-y—onto three-dimensional N-body simulations, using the MillenniumTNG suite as a benchmark. The goal of these methods is to produce fast and accurate reconstruction methods to aid future analyses of baryonic feedback using the SZ effect. The first approach employs a Gaussian process emulator to model the SZ quantities via a transfer function, while the second utilizes hybrid effective field theory (HEFT) to reproduce these quantities within the simulation. Our analysis involves comparing both methods to the true MillenniumTNG optical depth and Compton-y fields using several metrics, including the cross-correlation coefficient, power spectrum, and power spectrum error. Additionally, we assess how well the reconstructed fields correlate with dark matter haloes across various mass thresholds. The results indicate that the transfer function method yields more accurate reconstructions for fields with initially high correlations (r ≈ 1), such as between the optical depth and dark matter fields. Conversely, the HEFT-based approach proves more effective in enhancing correlations for fields with weaker initial correlations (r ∼ 0.5), such as between the Compton-y and dark matter fields. Lastly, we discuss extensions of our methods to improve the reconstruction performance at the field level.
Large-momentum effective theory is a physics-guided systematic expansion to calculate light-cone parton distributions, including collinear (PDFs) and transverse-momentum-dependent ones, at any fixed momentum fraction 𝑥 within a range of [𝑥 min , 𝑥 max ]. It theoretically solves the ill-posed inverse problem that afflicts other theoretical approaches to collinear PDFs, such as short-distance factorizations. Recently, Dutrieux et al. raised practical concerns about whether current or even future lattice data will have sufficient precision in the subasymptotic correlation region to support an error-controlled extrapolation—and if not, whether it becomes an inverse problem where the relevant uncertainties cannot be properly quantified. While we agree that not all current lattice data have the desired precision to qualify for an asymptotic extrapolation, some calculations do, and more are expected in the future. We comment on the analysis and results in Dutrieux et al. and argue that a physics-based systematic extrapolation still provides the most reliable error estimates, even when the data quality is not ideal. In contrast, reframing the long-distance asymptotic extrapolation as a data-driven-only inverse problem with ad hoc mathematical conditioning could lead to unnecessarily conservative errors.
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The Thermo Scientific P385 Neutron Generator is a compact neutron source, producing 14 MeV neutrons through the deuterium-tritium (DT) fusion reaction. It is important to measure and understand the dependence of the neutron production rate on the accelerator current and voltage. In this study we evaluated neutron production with an absolutely calibrated liquid scintillator neutron spectrometer (BTI N-Probe), an absolutely calibrated He-3 detector surrounded by HDPE shells (Detec Nested Neutron Spectrometer, NNS), and two uncalibrated ZnS fast neutron scintillators (EJ-410), for both A3082 and A3083 sealed tubes. Here we also modeled the neutron yield using the TRIM code, which calculates the trajectory and the energy loss of deuterons and tritons within the target. Experimental results showed an essentially linear dependence on beam current, as expected. A 3.59 ±0.08 power law dependence on the operating voltage was measured, in effective agreement with the modeled value of 3.5. A series of absolute NNS and N-Probe measurements, matched against MCNP calculations, showed that the A3083 and A3082 tubes provide a maximum neutron yield of 8.2 × 10 8 n/s and 4.7 × 10 8 n/s respectively, with estimated uncertainty of ±10%.We showed, through modeling, that tritium decay is not a significant consideration for tubes, such as these, with lifetimes of less than 10 years.
The first automatic twisting heliostat, with 8 m2 reflector, was completed and tested on-sun in January 2024. It was set up on a target-oriented dual-axis mount, with the target-axis aimed at a target 113 m to the west. The shape-twisting is purely automatic, made by a mechanical cam coupling to the cross-axis, which turns as the angle of incidence of the sun’s rays on the reflector. Three unsaturated images taken at different times of day were recorded of the sun on the target, reflected at angles of incidence of 5°, 49° and 68°. The measured FWHM of three images is very similar, about 1.09 m or 9.5 mrad, only slightly larger than an ideal solar disc, indicating that the different twisted reflector shapes are close to the ideal biconics needed to image the sun. The encircled energy measured for all three images was similar, 87% within approximately 1.30 m diameter. We report here also on a novel, tracking camera that employs a semitransparent beamsplitter fixed perpendicular to the mirror surface and to the plane of incidence. Over 2.5 hours of measurement of closed loop tracking, errors were ≤ 0.36 mrad rms, in both tracking axes.
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Deep learning neuroarchitecture and hyperparameter search are important in finding the best configuration that maximizes learned model accuracy. However, the number of types of layers, their associated hyperparameters, and the myriad of ways to connect layers poses a significant computational challenge in discovering ideal model configurations. Here, we assess two different approaches for neuroarchitecture search for a LeNet style neural network, one that uses a fixed-length approach where there is a preset number of possible layers that can be toggled on or off via mutation, and a variable-length approach where layers can be freely added or removed via special mutation operators. We found that the variable-length implementation trained better models while discovering unusual layer configurations worth further exploration.
High Performance Computing (HPC) applications rely heavily on code optimizations to achieve good performance on modern CPU and GPU architectures. Traditional Machine Learning auto-tuning approaches have demonstrated success in exploring high-dimensional spaces, but they often require expensive compile-run evaluations and lack adaptability for large HPC applications. The recent advances in Large Language Models (LLMs) and Agentic AI systems raise intriguing questions about the potential of these approaches to address specific optimization methodologies. This work aims to answer an essential question for the HPC community: “How Agentic AI Systems Compare to Traditional ML Autotuning Techniques?” To address this question, we present a comparative analysis between a traditional ML-based optimization approach and an Agentic AI system, evaluating their respective capabilities and limitations for loop-level optimization. In addition, we introduced a new Agentic AI system named LoopGen-AI using three different Large Language Models: GPT-4.1, Claude 4.0, and Gemini 2.5. A key finding is that LoopGen-AI achieves competitive per-formance with only a few program runs, the reasoning logs from the agents revealed that their decisions rely heavily on the combination of semantic understanding of the target kernel with dynamic feedback from the environment, highlighting a promising new dimension in performance tuning. In contrast, ML-based autotuners focus on statistical exploration, and require orders of magnitude more runs to reach peak performance. Additionally, our analysis shows that prompt engineering, particularly using Persona + Context Manager patterns, significantly impacts the effectiveness of Agentic AI. Our results indicate that while Agentic AI systems are not yet a complete replacement for ML-based autotuners, it can effectively complement traditional methods.
Projections and associated uncertainty estimates are generated for a variety of user-selectable EV charging sessions, electricity tariffs, subsidy levels, revenue schemes, charging station configurations, and on-site solar and/or storage options. The outputs are presented in CHIP's web portal browser in the form of easily interpretable graphics (interactive graphs and bar charts) that facilitate convenient comparison among different scenarios to aid decision-making. The user should bring assumptions for modeling on simulation planning horizon, number of EV charging sessions per year, electricity costs (energy and demand charge rates; flat versus time-of-use rate), site capital costs (equipment for EV chargers and transformer), solar PV, and battery energy storage (kW).