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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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At least 109 records · Page 6

Reliable p K a Prediction through Efficient Incorporation of Anharmonicity within the Nuclear–Electronic Orbital Framework

Accurate pK a prediction is critical for understanding chemical reactivity and molecular properties across a wide range of applications. Computational approaches usually invoke a harmonic treatment of the vibrational modes for zero-point energies, as well as thermal and entropic contributions. Herein, we present a general protocol for relative pK a prediction that incorporates the significant anharmonic effects using nuclear–electronic orbital (NEO) theory. This protocol is validated against experimental data for a range of molecules in acetonitrile, including protonated nitrogen bases, nitrophenols, anilines, and diamines, as well as cobalt electrocatalysts. For simple acids, the NEO approach offers only a slight improvement over conventional density functional theory with the standard harmonic vibrational treatment, whereas for hydrogen-bonded acids, the NEO approach offers more significantly improved performance at a comparable computational cost. This accessible methodology provides a practical route for accurate pKa prediction in challenging systems and is extendable to related thermodynamic properties such as hydricities and proton-coupled redox potentials.

Density functional theory↗

Improved Bayesian regularization of inverse problems in vibrations and acoustics using noise-only measurements

Here, this paper studies Tikhonov regularization (ridge regression) parameter selection for problems in vibrations and acoustics. The selection method is based on a popular Bayesian method, but it incorporates measurements of sensor noise. The regularization parameter is closely related to the ratio of system input energy to noise energy, so noise measurements inform the inference procedure and improve parameter identification. In cases where standard Bayesian regularization identifies zero as the optimal regularization parameter, noise measurements guarantee a unique nonzero optimum. Sufficient theoretical criteria are developed for this guarantee. The method is verified in even-determined and under-determined configurations in an acoustic source localization simulation and a vibration load identification experiment. It is shown to yield significant improvements over existing empirical Bayesian regularization. Improvements are larger in the even-determined case and smaller in the under-determined case, wherein the inverse solution is less sensitive to the regularization parameter.

42 ENGINEERING↗

Microbiome data management in action workshop: Atlanta, GA, USA, June 12–13, 2024

Microbiome research is revolutionizing human and environmental health, but the value and reuse of microbiome data are significantly hampered by the limited development and adoption of data standards. While several ongoing efforts are aimed at improving microbiome data management, significant gaps still remain in terms of defining and promoting adoption of consensus standards for these datasets. The Strengthening the Organization and Reporting of Microbiome Studies (STORMS) guidelines for human microbiome research have been endorsed and successfully utilized by many research organizations, publishers, and funding agencies, and have been recognized as a consensus community standard. No equivalent effort has occurred for environmental, synthetic, and non-human host-associated microbiomes. To address this growing need within the microbiome research community, we convened the Microbiome Data Management in Action Workshop (June 12–13, 2024, in Atlanta, GA, USA), to bring together key decision makers in microbiome science including researchers, publishers, funders, and data repositories. The 50 attendees, representing the diverse and interdisciplinary nature of microbiome research, discussed recent progress and challenges, and brainstormed actionable recommendations and paths forward for coordinated environmental microbiome data management and the modifications necessary for the STORMS guidelines to be applied to environmental, non-human host, and synthetic microbiomes. The outcomes of this workshop will form the basis of a formalized data management roadmap to be implemented across the field. These best practices will drive scientific innovation now and in years to come as these data continue to be used not only in targeted reanalyses but in large-scale models and machine learning efforts.

54 ENVIRONMENTAL SCIENCES↗

Nyström type exponential integrators for strongly magnetized charged particle dynamics

Solving for charged particle motion in electromagnetic fields (i.e. the particle pushing problem) is a computationally intensive component of particle-in-cell (PIC) methods for plasma physics simulations. This task is especially challenging when the plasma is strongly magnetized due numerical stiffness arising from the wide range of time scales between highly oscillatory gyromotion and long term macroscopic behavior. A promising approach to solve these problems is by a class of methods known as exponential integrators that can solve linear problems exactly and are A-stable. This work extends the standard exponential integration framework to derive Nyström-type exponential integrators that integrates the Newtonian equations of motion as a second-order differential equation directly. In particular, we derive second-order and third-order Nyström-type exponential integrators for strongly magnetized particle pushing problems. Numerical experiments show that the Nyström-type exponential integrators exhibit significant improvement in computation speed over the standard exponential integrators.

general physics↗

Constraining the Higgs potential with neural simulation-based inference for di-Higgs production

Determining the form of the Higgs potential is one of the most exciting challenges of modern particle physics. Higgs pair production directly probes the Higgs self-coupling and should be observed in the near future at the High-Luminosity LHC. We explore how to improve the sensitivity to physics beyond the Standard Model through per-event kinematics for di-Higgs events. In particular, we employ machine learning through simulation-based inference to estimate per-event likelihood ratios and gauge potential sensitivity gains from including this kinematic information. In terms of the Standard Model Effective Field Theory, we find that adding a limited number of observables can help to remove degeneracies in Wilson coefficient likelihoods and significantly improve the experimental sensitivity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Similarity Metric for Data Optimization and Efficient Training of Reactive Machine Learning Force Fields for Hydrocarbon Radiolysis

Radiolysis is a common approach to sterilize polymers, chemically modify them for upcycling, and accelerate their decomposition for recycling purposes. Reactive molecular dynamics (MD) simulations provide a powerful tool to generate atomic-level trajectories of the reactive processes and quantify radiolytic chemical degradation pathways. For this, machine learning (ML) surrogate models for reactive force fields with quantum mechanical accuracy are now widely used, which require ML training data sets that can provide information on atomic environments for target chemical systems. However, radiolysis chemistry can be highly complex and diverse, which poses significant challenges for generating training data to parametrize ML models. In this regard, we developed a method for optimizing the training data set using a cosine similarity metric to help guide training set selection for radiolysis of polyethylene, a model hydrocarbon polymer, as well as to enhance the transferability of our reactive ML force field (MLFF) to a variety of molecular and polymeric systems. Our approach performs atom-by-atom comparisons between local atomic environments to pinpoint important data points associated with rare and localized events, such as radiolysis damage within structures. We apply this approach to train the Chebyshev Interaction Model for Efficient Simulation (ChIMES) MLFF model, which expresses the atomic interaction potentials in terms of linear combinations of many-body Chebyshev polynomials. We first show that our method can reduce our training set size by ∼70% while improving overall accuracy compared to more standard MD model fitting approaches. We then validate our optimum model against diverse hydrocarbon simulation data, including simple alkanes and systems with unsaturated carbon bonds, over a wide range of thermodynamic conditions. Finally, we use our ChIMES model to perform MD simulations of radiolytic damage with large-scale systems that help avoid system size effects. Overall, our approach yields an MD force field that retains most of the accuracy of the underlying quantum method while yielding many orders of improvement in computational efficiency. In conclusion, our efforts will have impact on future hydrocarbon polymer radiolysis studies, where the chemical details of the polymer–radiation interactions can have a strong effect on the resulting products observed in experiments.

Hydrocarbons↗

Cybersecurity Risk Profiles for Distributed Energy Resource Management Systems

Managing the digitalization of increasingly diversity energy resources is a complex challenge for energy systems planners and managers. As the penetration of solar photovoltaics (PV) and other distributed renewable energy resources (DERs) expands, distributed energy resource management systems (DERMS) will play an increasingly important role in managing, monitoring, and controlling DERs as electric systems before more distributed, interconnected, and networked. However, the cybersecurity implications of DERMS deployments are not well understood today. A lack of understanding around the cybersecurity implications of DERMS deployments and variability in the security posture of DERMS vendors, owners, and operators could introduce new security risks to evolving electric power systems. This paper describes cybersecurity attack scenarios on DERMS, identifies related cybersecurity standards and guidelines, reviews the security features of state-of-the-art DERMS solutions, and offers cybersecurity guidance for DERMS vendors, owners, and operators to protect DERMS' unique capabilities. Standardizing cybersecurity requirements for DERMS could help improve the security of DERMS integrations and improve innovations that are more secure by design. The cybersecurity guidance found in this paper is intended to offer a unified approach and lay the foundation for future standardization of DERMS cybersecurity to reduce risk to the solar industry and other renewable energy stakeholders when integrating these technologies with electric power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Spin squeezing of macroscopic nuclear spin ensembles

Spin squeezing has been explored in atomic systems as a tool for quantum sensing, improving experimental sensitivity beyond the spin standard quantum limit for certain measurements. To optimize absolute metrological sensitivity, it is beneficial to consider macroscopic spin ensembles, such as nuclear spins in solids and liquids. Coupling a macroscopic spin ensemble to a parametrically-modulated resonant circuit can create collective spin squeezing by generating spin correlations mediated by the circuit. We analyze the squeezing dynamics in the presence of decoherence and finite spin polarization, showing that achieving 7 dB spin squeezing is feasible in several nuclear spin systems. The metrological benefit of squeezing a macroscopic spin ensemble lies in the suppression of technical noise sources in the spin detection system relative to the spin projection noise. This expands the experimental sensitivity bandwidth when searching for signals of unknown frequency and can improve the resonant signal-to-noise ratio. Squeezing macroscopic spin ensembles may prove to be a useful technique for fundamental physics experiments aimed at detecting spin interactions with oscillating background fields, such as ultralight dark matter. Published by the American Physical Society 2025

Boyers, Eric↗

Probabilistic flux limiters

The stable numerical integration of shocks in compressible flow simulations relies on the reduction or elimination of Gibbs phenomena (unstable, spurious oscillations). A popular method to virtually eliminate Gibbs oscillations caused by numerical discretization in under-resolved simulations is to use a flux limiter. A wide range of flux limiters have been studied in the literature, with recent interest in their optimization via machine learning methods trained on high-resolution datasets. The common use of flux limiters in numerical codes as plug-and-play blackbox components makes them key targets for design improvement. Even for deterministic dynamical models, numerical uncertainty is introduced via coarse-graining required by insufficient computational power to solve all scales of motion. Conventional flux limiters are deterministic and lack the capacity to address uncertainties, both aleatoric (inherent randomness) and epistemic (modeling uncertainty due to limited knowledge), which arise in coarse-grained numerical simulations. Here, we introduce a conceptually distinct type of flux limiter that is designed to handle the effects of randomness in the model and uncertainty in model parameters. Unlike traditional single-function flux limiters, these new probabilistic flux limiters incorporate multiple flux limiting functions, each applied with a learned probability drawn from high-resolution data to mitigate the effects of uncertainty in numerical simulations. This approach departs from traditional single-function limiters by explicitly modeling and incorporating uncertainty into the shock capturing process. Using the example of Burgers' equation as a testbed, we show that a machine learned, probabilistic flux limiter may be used in a shock capturing code to more accurately capture shock profiles. In particular, we show that our probabilistic flux limiter outperforms standard limiters and can be successively improved upon (up to a point) by expanding the set of probabilistically chosen flux limiting functions.

97 MATHEMATICS AND COMPUTING↗

Deep learning of structural morphology imaged by scanning X-ray diffraction microscopy

Scanning X-ray nanodiffraction microscopy is a powerful technique for spatially resolving nanoscale structural morphologies by diffraction contrast. One of the critical challenges in experimental nanodiffraction data analysis is posed by the convergence angle of nanoscale focusing optics which creates simultaneous dependency of the far-field scattering data on three independent components of the local strain tensor-corresponding to dilation and two potential rigid body rotations of the unit cell. All three components are in principle resolvable through a spatially mapped sample tilt series; however, traditional data analysis is computationally expensive and prone to artifacts. In this study, we implement NanobeamNN, a convolutional neural network specifically tailored to the analysis of scanning probe X-ray microscopy data. NanobeamNN learns lattice strain and rotation angles from simulated diffraction of a focused X-ray nanobeam by an epitaxial thin film and can directly make reasonable predictions on experimental data without the need for additional fine-tuning. We demonstrate that this approach represents a significant advancement in computational speed over conventional methods, as well as a potential improvement in accuracy over the current standard.

Luo, Aileen [Cornell Univ., Ithaca, NY (United Sta↗

ZTF SN Ia DR2 follow-up: Exploring the origin of the Type Ia supernova host galaxy step through Si II velocities

The relation between Type Ia supernovae (SNe Ia) and the stellar masses of their host galaxy is well documented. In particular, Hubble residuals display a distinct luminosity shift based on host mass. This is known as the mass step. This effect is widely used as an additional correction factor in the standardisation of SN Ia luminosities. We investigate the Hubble residuals and the mass step of normal SNe Ia in the context of Si IIλ6355 velocities based on 277 normal SNe Ia that are near their peak in the second data release (DR2) of the Zwicky Transient Facility (ZTF). We divided the sample into high-velocity (HV) and normal-velocity (NV) SNe Ia, separated at 12,000 km s −1 . This produced a sample of 70 HV and 207 NV objects. We then explored potential environment- and/or progenitor-related effects by investigating the Si IIλ6355 velocities with parameters such as the light-curve stretch x 1 , the colour c, and the host galaxy properties. Although we only find a marginal difference between the Hubble residuals of HV and NV SNe Ia, the NV mass step is 0.149 ± 0.024 mag (6.3σ). The HV mass step is smaller, 0.046 ± 0.041 mag (1.1σ), and is consistent with zero. The difference between the NV and HV mass steps is modest, at ∼2.2σ. Moreover, the clearest subtype difference appears for SNe in central regions (d DLR < 1), where NV SNe Ia show a large mass step, whereas HV SNe Ia are consistent with no step, yielding a difference of 3.1–3.6σ between NV and HV SNe Ia. We observe a host-colour step for both subtypes. NV SNe Ia show a step of 0.142 ± 0.024 mag (5.9σ), while HV SNe Ia show a step of 0.158 ± 0.042 mag (3.8σ), where the HV SNe Ia step appears to be larger, but the significance is lower because the sample size is smaller. Overall, the NV and HV colour steps are statistically consistent. HV SNe Ia also show modest (∼2.5–3σ) steps in certain subsets, such as those in outer regions (d DLR > 1), whereas NV SNe display stronger environmental trends. Our results indicate that NV SNe Ia appear to be more environmentally sensitive, particularly in central likely metal-rich and older regions, while HV SNe Ia show weaker and subset-dependent trends. This suggests that applying a universal mass-step correction might introduce biases, and that incorporating refined classifications and/or environment-dependent factors, such as the location within the host, might improve future cosmological analyses beyond the standard x 1 and c cuts.

supernovae: general↗

Real classical shadows

Efficiently learning expectation values of a quantum state using classical shadow tomography has become a fundamental task in quantum information theory. In a classical shadows protocol, one measures a state in a chosen basis $\mathcal{W}$ after it has evolved under a unitary transformation randomly sampled from a chosen distribution $\mathcal{U}$. In this work we study the case where $\mathcal{U}$ corresponds to either local or global orthogonal Clifford gates, and $\mathcal{W}$ consists of real-valued vectors. Our results show that for various situations of interest, this ‘real’ classical shadow protocol improves the sample complexity over the standard scheme based on general Clifford unitaries. For example, when one is interested in estimating the expectation values of arbitrary real-valued observables, global orthogonal Cliffords typically decrease the required number of samples by a factor of two. More dramatically, for k-local observables composed only of real-valued Pauli operators, sampling local orthogonal Cliffords leads to a reduction by an exponential-in-k factor in the sample complexity over local unitary Cliffords. Finally, we show that by measuring in a basis containing complex-valued vectors, orthogonal shadows can, in the limit of large system size, exactly reproduce the original unitary shadows protocol.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Monte Carlo Event Generation with Continuous Normalizing Flows

We apply continuous normalizing flows trained with the flow matching method to the problem of phase-space sampling in Monte Carlo event generation for high-energy collider physics. Focusing on lepton-pair and top-quark pair production with multiple jets, the two computationally most expensive processes at the Large Hadron Collider, we train helicity-conditioned continuous normalizing flows to remap the random numbers used in matrix element evaluation. Compared to standard methods, we achieve unweighting efficiency improvements by factors of up to 184 and 25 for the two processes at their respective highest jet number, at the cost of an increased evaluation time. When combining the advantages of continuous normalizing flows with the fast evaluation times of coupling-layer-based flows, using the RegFlow approach, we find parton-level unweighted event generation walltime gains of about a factor of 10 at the highest jet numbers. These substantial gains highlight the promise of samplers based on machine learning for next-generation collider experiments.

Bothmann, Enrico [CERN; Gottingen U.] (ORCID:00000↗

Uniformly decaying subspaces for error-mitigated quantum computation

Here, we present a general condition to obtain subspaces that decay uniformly in a system governed by the Lindblad master equation and use them to perform error-mitigated quantum computation. The expectation values of dynamics encoded in such subspaces are unbiased estimators of noise-free expectation values. In analogy to the decoherence free subspaces which are left invariant by the action of Lindblad operators, we show that the uniformly decaying subspaces are left invariant (up to orthogonal terms) by the action of the dissipative part of the Lindblad equation. We apply our theory to a system of qubits and qudits undergoing relaxation with varying decay rates and show that such subspaces can be used to eliminate bias up to first-order variations in the decay rates without requiring full knowledge of noise. Since such a bias cannot be corrected through standard symmetry verification, our method can improve error mitigation in dual-rail qubits and, given partial knowledge of noise, can perform better than probabilistic error cancellation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Utility of a hybrid approach to the hadronic vacuum polarization contribution to the muon anomalous magnetic moment

An accurate calculation of the leading-order hadronic vacuum polarization (LOHVP) contribution to the anomalous magnetic moment of the muon ( a μ ) is key to determining whether a discrepancy, suggesting new physics, exists between the Standard Model and experimental results. This calculation can be expressed as an integral over Euclidean time of a current-current correlator G ( t ) , where G ( t ) can be calculated using lattice QCD or, with dispersion relations, from experimental data for e + e − → hadrons . The BMW/DMZ collaboration recently presented a hybrid approach in which G ( t ) is calculated using lattice QCD for most of the contributing t range, but using experimental data for the largest t (lowest energy) region. Here we study the advantages of varying the position t = t 1 separating lattice QCD from data-driven contributions. The total LOHVP contribution should be independent of t 1 , providing both a test of the experimental input and the robustness of the hybrid approach. We use this criterion and a correlated fit to show that Fermilab/HPQCD/MILC lattice QCD results from 2019 strongly favor the CMD-3 cross section data for e + e − → π + π − over a combination of earlier experimental results for this channel. Further, the resulting total LOHVP contribution obtained is consistent with the result obtained by BMW/DMZ, and supports the scenario in which there is no significant discrepancy between the experimental value for a μ and that expected in the Standard Model. We then discuss how improved lattice results in this hybrid approach could provide a more accurate total LOHVP across a wider range of t 1 values with an uncertainty that is smaller than that from either lattice QCD or data-driven approaches on their own. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Controlling Oxidation of Nb in Oxygen Abundant Environments.

Superconducting Radio Frequency (SRF) cavities, the core of modern particle accelerators, rely on high-purity niobium (Nb) to achieve high quality factors and accelerating gradients. However, native oxidation in ambient conditions introduces a complex oxide layer with increased surface defects and contamination, particularly from carbon. This research explores a controlled oxidation processes in an oxygen-abundant environment to try to control this complex oxide stack, with the goal of improving surface purity and uniformity. Using standard surface preparation techniques—including electropolishing, vacuum baking, and sonication—followed by a custom oxidation protocol, we compared structural and chemical changes through Confocal Microscopy, Scanning Electron Microscopy (SEM), and X-Ray Photoelectron Spectroscopy (XPS). Results show that iterative oxidation cycles can significantly reduce carbon-related defects, even without baking. Aditionally, baked samples with a specific oxidation technique demonstrated a measurable 25\% decrease in carbide formation.

Romero, Juan [Fermilab]↗

Quantum-amplified global-phase spectroscopy on an optical clock transition

Optical lattice clocks are at the forefront of precision metrology, operating near a standard quantum limit set by quantum noise. Harnessing quantum entanglement offers a promising route to surpass this limit; however, there are practical difficulties in terms of scalability and measurement resolution requirements. Here we adapt the holonomic quantum gate concept to develop a new Rabi-type ‘global-phase spectroscopy’ that uses the detuning-sensitive global Aharonov–Anandan phase19. With this approach, we can demonstrate quantum-amplified time-reversal spectroscopy on an optical clock transition that achieves directly measured 2.4(7) dB metrological gain and 4.0(8) dB improvement in laser noise sensitivity beyond the standard quantum limit. To this end, we introduce rotary echo to protect the dynamics from inhomogeneities in light–atom coupling and implement a laser-noise-cancelling differential measurement through symmetric phase encoding in two nuclear spin states. Our technique is not limited by measurement resolution, scales easily because of the global nature of entangling interaction and exhibits high resilience to typical experimental imperfections. As a result, we expect it to be broadly applicable to next-generation atomic clocks and other quantum sensors approaching the fundamental quantum precision limits.

Zaporski, Leon [Massachusetts Institute of Technol↗

How to Design and Implement an Equitable Building Performance Standard: Lessons from the Building Performance Standards Technical Assistance Network

States and local governments seek to accomplish the intersecting goals of reducing greenhouse gas emissions, improving building operations, and bettering the daily lives of their communities. Building Performance Standards (BPS) have emerged as a critical policy lever to reach these intertwined climate and societal goals. These policies, if shaped and implemented well, have the chance to not only help reach our nation's climate, energy, and livability goals, but to do so with the active participation of those traditionally excluded from policy processes. This nascent policy movement provides jurisdictions across the country the opportunity to shape these policies from the outset to deliver comfort, health, and safety in our built environment for all. The US Department of Energy in partnership with our National Laboratories have been providing technical assistance for jurisdictions interested in, adopting, and implementing Building Performance Standards. Through this work, the BPS Technical Assistance Network (TA Network) has tracked and documented the innovative and equitable approaches to BPS across the country. The TA Network has crafted foundational technical analysis, such as building stock and emissions impacts, equity prioritization and peak load impacts, and aggregated cost benefit analysis. And by combining powerful technical analysis with dissemination of best practices resources to support equitable implementation, the TA Network provides jurisdictions with the tools and support necessary to embark on their ambitious policy goals.

building performance standards↗