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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Electronic structure theory with molecular point group symmetries on quantum annealers

Quantum computation has the potential to revolutionize quantum chemistry through major speedups in computation times and an exponential reduction in computational resources. Here, we combine the symmetry-adapted Jordan–Wigner encoding based on the full Boolean symmetry group $\mathbb{Z}$$^{k}_{2}$ with our new implementation of the Xia–Bian–Kais (XBK) method for improving the efficiency of electronic structure theory calculations on quantum annealers, particularly by reducing the number of qubits needed to achieve the same accuracy. By providing a more extensive symmetry-adapted encoding (SAE) than previous work, we are able to simulate molecules larger than those previously reported that have been studied using methods developed for quantum annealers and without using an active space. We calculated the potential energy surfaces of H 2 , LiH, He 2 , H 2 O, O 2 , N 2 , Li 2 , F 2 , CO, BH 3 , NH 3 , and CH 4 , with the largest molecule in the STO-6G basis set requiring 16 qubits with our SAE, and compared them with full configuration interaction results. The application of SAE to the XBK method provides an exponential reduction in the size of the Hilbert space and scales well with the size of the problem. It does not introduce significant additional errors for even or large values of a key variational parameter that determines the number of ancilla qubits used in the XBK method’s Hamiltonian embedding, or for certain molecules such as He 2 and H 2 O. Here, we provide an explanation for this behavior and a recommendation on the usage of our method. In addition, we briefly discuss the potential of extracting electronic excited states from our method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

ChemEcho v1.0

ChemEcho is a tool that converts tandem mass spectra into embeddings used to build machine learning (ML) models with fully explainable predictions. It provides an API for transforming raw tandem mass spectral data into embeddings, along with functions for training and validating ML models. Additionally, it includes utilities for retrieving and cleaning training data. ChemEcho is broadly applicable in ML pipelines that use tandem mass spectra for a variety of tasks, such as chemical classification or bioactivity mining. While there are existing methods to generate embeddings from fragmentation data, ChemEcho's approach ensures that predictions remain interpretable, enabling experts to evaluate results and generate hypotheses about the underlying data.

Harwood, Thomas [Lawrence Berkeley National Labora↗
DOE OSTI2025

Topological Signatures of Adversaries in Multimodal Alignments

Topological Data Analysis for Adversarial Detection (LANL O4937) - Detects adversarial examples in vision-language models using persistent homology and two-sample testing. Combines TDA features from CLIP embeddings with statistical methods (ME, SCF, SAMMD, C2ST) for robust detection across ImageNet, CIFAR-10/100.

Bhattarai, Manish↗
DOE OSTI2026

Thriving in the Carbon-Aware Market: How to Account for Emissions in the Era of Carbon-Centered Trade Policies

Emerging global policies, such as the European Union's enacted Carbon Border Adjustment Mechanism and similar policies under development in Canada, Australia and the United Kingdom, will place a premium on goods traded into their territories with higher embedded emissions than those produced domestically. U.S. manufacturers could stand to benefit from such policies; given the investments U.S. industry has made to reduce the energy and emission intensities of its operations. For example, the overall GHG intensity of U.S. steel production in 2019 was ~0.96 t CO2/t steel, less than half that of China (~1.97 t CO2/t steel), and bested only by Italy. To realize these benefits, transparent, accurate, interoperable and accepted embedded emissions accounting and calculation methods are required. Achieving this requires overcoming challenges related to data availability, boundary definitions, and product definitions among others, both at individual facilities as well as through value chains. We will present technical findings on methodology considerations and data-availability constraints for determining the emissions of traded goods, using steel as a pilot and leveraging publicly available data. Issues such as determining the appropriate scope for emissions accounting, implications of the specificity of product chosen, emissions allocation in multi-product facilities, and enumeration of emissions for products manufactured across multiple facilities will be discussed. By sharing the results of our efforts, we aim to inform the development and execution of embedded emissions accounting methods from a technical perspective such that U.S. manufacturers can thrive in emerging global markets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Machine learning prediction of enzyme optimum pH

The relationship between pH and enzyme catalytic activity, especially the optimal pH (pH opt ) at which enzymes function, is critical for biotechnological applications. Hence, computational methods to predict pH opt will enhance enzyme discovery and design by facilitating accurate identification of enzymes that function optimally at specific pH levels, and by elucidating sequence-function relationships. Here, in this study, we proposed and evaluated various machine learning methods for predicting pH opt , conducting extensive hyperparameter optimization and training over 11,000 model instances. Our results demonstrate that models utilizing language model embeddings markedly outperform other methods in predicting pHopt. We present EpHod, the best-performing model, to predict pHopt, making it publicly available to researchers. From sequence data, EpHod directly learns structural and biophysical features that relate to pH opt , including proximity of residues to the catalytic centre and the accessibility of solvent molecules. Overall, EpHod presents a promising advancement in pH opt prediction and will potentially speed up the development of enzyme technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Enhanced electronic sensors

A micro-structured device that can improve sensitivity and signal-to-noise for electronic sensor materials is embedded in electrically resistive materials. The technology includes a three-dimensional embedded electrode structure and fabrication methods for making the device for electronic sensing in bulk resistive materials. Embedded electrode structures address issues in conventional sensors by allowing independent control of sensitive material thickness, area, electric field intensity, and field direction.

Doty, Fred Patrick↗
DOE OSTI2025

Optimal Control for Fast Frequency Response and Black-Start using Embedded Storages with Grid-Forming Control

The grid-forming inverter is regarded as the solution for integrating high levels of renewable resources into future power systems. Ensuring the stable operation of grids necessitates that grid-forming inverters offer fast frequency response. This report introduces an optimal control method that coordinates the embedded storage within the grid-forming control model with conventional synchronous generators. The optimized active power reference for the embedded storage is generated using receding horizon optimization control, aiming to keep the center of inertia frequency within acceptable limits. The effectiveness of the proposed control is verified through testing in the IEEE 39-bus system. In addition, with grid-forming capability, we will also investigate the application of using mobile embedded storages as black-start units to provide cranking power to energize non-blackstart generators in a black-start process.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Direct detection system for full-field nanoscale X-ray diffraction-contrast imaging

Recent developments in X-ray science provide methods to probe deeply embedded mesoscale grain structures and spatially resolve them using dark field X-ray microscopy (DFXM). Extending this technique to investigate weak diffraction signals such as magnetic systems, quantum materials and thin films prove challenging due to available detection methods and incident X-ray flux at the sample. We present a direct detection method developed in conjunction with KAImaging which focuses on DFXM studies in the hard X-ray range of 10s of keV and above capable of approaching nanoscale resolution. Additionally, we compare this direct detection scheme with routinely used scintillator-based optical detection and achieve an order of magnitude improvement in exposure times allowing for imaging of weakly diffracting ordered systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Ab Initio Many Body Quantum Embedding and Local Correlation in Crystalline Materials using Interpolative Separable Density Fitting

We present an efficient implementation of ab initio many-body quantum embedding and local correlation methods for infinite periodic systems through translational symmetry adapted interpolative separable density fitting, an approach which reduces the scaling of the calculations to only linear with the number of k-points. Employing this methodology, we compute correlated ground-state coupled cluster energies within density matrix embedding and local natural orbital correlation frameworks for both weakly and strongly correlated solids, using up to 1000 k-points. By extrapolating the local correlation domains and k-point sampling we further obtain estimates of the full coupled cluster with singles, doubles, and perturbative triples ground-state energies in the thermodynamic limit.

Chemical Physics (physics.chem-ph)↗
DOE OSTI2025

Embedded random phase approximation for magnetic systems: H 2 dissociative adsorption on Fe(110)

The random phase approximation (RPA), a method for treating electron correlation, has been shown to be superior to standard density functional theory (DFT) approximations in numerous cases. However, the RPA’s computational cost is substantially higher than that of DFT, particularly restricting its application to extended surfaces. The recently introduced embedded RPA (emb-RPA) approach [Wei et al., J. Chem. Phys. 159(19), 194108 (2023)] reduces this computational cost by approximately two orders of magnitude. While previous applications of emb-RPA focused on non-spin-polarized systems, here we extend the approach to ferromagnetic ones. Unlike other embedded correlated wavefunction methods, such as embedded complete active space self-consistent field theory, emb-RPA is advantageous for spin-polarized systems because the RPA is compatible with unrestricted DFT solutions, which are eigenfunctions of the spin angular momentum operator S z but not the total spin-squared operator S 2 . By applying emb-RPA with specific magnetization constraints, we achieved a speedup of two to three orders of magnitude (one order when accounting for the one-time embedding potential optimization cost) with only small errors (∼50 meV) compared to full periodic RPA. Moreover, emb-RPA significantly reduces the over-binding errors of DFT approximations. In conclusion, we anticipate that the acceleration enabled by the spin-polarized emb-RPA approach will broaden the applicability of RPA to magnetic materials.

Density functional theory↗
DOE OSTI2026

ZTF-SEDm Type Ia supernova sample for Twins Embedding spectrophotometric standardization

Aims. This paper has two aims: the first aim is to build a large homogeneous spectrophotometric sample of Type Ia supernovae (SNe Ia) from the second data release of the Zwicky Transient Facility (ZTF DR2). We used the spectrum sample from the low-resolution ( R ∼ 100) SEDmachine (SEDm) Integral Field Spectrograph (IFS) that gathered 3069 spectra. This is one of the largest samples of such collections that can attempt to reproduce the Twins Embedding (TE) spectrophotometric standardization method. This is our second objective. The method was developed based on high-quality spectra from 200 SNe Ia of the Nearby Supernova factory (SNfactory) and led to an exceptionally low value of 0.073 mag for the intrinsic scatter. Methods. As the SEDm is not designed as a spectrophotometric instrument, we first improved the flux-calibration accuracy of the SN Ia spectrum sample using the ZTF photometric data, which were calibrated at the percent level. We corrected the spectra for second-order polynomials, fitted by comparing the synthetic photometry in the ZTF g , r , i filters with the light-curve (LC) data. We then applied the three steps of the TE parameterization to a subset of 783 ZTF SN spectra near maximum light while comparing results from SNfactory and ZTF. We finally analyzed the standardization methods based on the TE parameters. Results. The precision of the phase-correction model, which is the first step of the TE, is estimated at 0.01 mag in g band based on ZTF data. Despite the challenge posed by the spectrum-extraction pipeline associated with the SEDm (flux calibration, leftover host signal, low signal-to-noise ratio, and low resolution), we applied a first standardization in color based on the second step of the TE, called read between the lines (RBTL), to the ZTF sample. We reached a Hubble residual scatter of 0.153 mag, all in normalized median absolute deviation, which is to be compared to the ∼0.11 mag obtained with the SNfactory data. The SALT color and stretch standardization reaches a scatter of 0.164 mag for the same ZTF SN Ia sample, and its host steps are ∼0.1 mag and zero for RBTL. When considering the scatter due to the redshift error and flux calibration error, we estimated a RBTL scatter of ∼0.129 mag for this ZTF sample as an upper limit because we identified an additional contribution from a systematic error in color. We tested the standardization based on the nonlinear TE parameters, and, as expected from the low spectrum quality, it did not improve the overall dispersion. Conclusions. We release 1897 flux calibrated spectra of 1607 SNe Ia with an estimated photometric accuracy of 0.07 mag. We further demonstrate that some amount of spectrophotometric SN Ia standardization can be done with limited-quality spectra. The RBTL standardization is more efficient than that of SALT with one parameter less, and the resulting host steps are consistent with zero. This makes it less prone to astrophysical bias. For future spectroscopic surveys, targeting the extraction pipeline for a thorough flux calibration and good signal-to-noise ratio would enable us to compute the full TE standardization, which would further reduce the scatter in the distance estimate.

Ganot, C↗
DOE OSTI2026

Exploring the Feasibility of Modern Fiber Optics in Offshore Mooring Condition Monitoring

This project explored the feasibility of using fiber-optic sensing technologies for condition monitoring of offshore synthetic mooring lines used in marine renewable energy systems. The work focused on evaluating optical fiber materials, interrogation methods, and rope-embedment approaches capable of enabling distributed strain or elongation measurements along mooring lines.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

A graph embedding‐based approach for automatic cyber‐physical power system risk assessment to prevent and mitigate threats at scale

Abstract Power systems are facing an increasing number of cyber incidents, potentially leading to damaging consequences to both physical and cyber aspects. However, the development of analytical methods for the study of large‐scale power infrastructures as cyber‐physical systems is still in its early stages. Drawing inspiration from machine‐learning techniques, the authors introduce a method inspired by the principles of graph embedding that is tailored for quantitative risk assessment and the exploration of possible mitigation strategies of large‐scale cyber‐physical power systems. The primary advantage of the graph embedding approach lies in its ability to generate numerous random walks on a graph, simulating potential access paths. Meanwhile, it enables capturing high‐dimensional structures in low‐dimensional spaces, facilitating advanced machine‐learning applications, and ensuring scalability and adaptability for comprehensive network analysis. By employing this graph embedding‐based approach, the authors present a structured and methodical framework for risk assessment in cyber‐physical systems. The proposed graph embedding‐based risk analysis framework aims to provide a more insightful perspective on cyber‐physical risk assessment and situation awareness for power systems. To validate and demonstrate its applicability, the method has been tested on two cyber‐physical power system models: the Western System Coordinating Council (WSCC) 9‐Bus System and the Illinois 200‐Bus System , thereby showing its advantages in enhancing the accuracy of risk analysis and comprehensiveness of situational awareness.

Sun, Shining↗
DOE OSTI2025

Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning

Seismic data recorded at industrial sites contain valuable information on anthropogenic activities. With advances in machine learning and computing power, new opportunities have emerged to explore the seismic wavefield in these complex environments. We applied two unsupervised machine learning algorithms to analyze continuous seismic data collected from an industrial facility in Texas, United States. The Uniform Manifold Approximation and Projection for Dimension Reduction algorithm was used to reduce the dimensionality of the data and generate 2D embeddings. Then, the Hierarchical Density-Based Spatial Clustering of Applications with Noise method was employed to automatically group these embeddings into distinct signal clusters. Our analysis of over 1400 hr (around 59 days) of continuous seismic data revealed five and seven signal clusters at two separate stations. At both stations, we identified clusters associated with background noise and vehicle traffic, with the latter’s temporal patterns aligning closely with the facility’s work schedule. Furthermore, the algorithms detected signal clusters from unknown sources and underline the ability of unsupervised machine learning for uncovering previously unrecognized patterns. Our analysis demonstrates the effectiveness of unsupervised approaches in examining continuous seismic data without requiring prior knowledge or pre-existing labels.

58 GEOSCIENCES↗
DOE OSTI2024

Optimal Zeno Dragging for Quantum Control: A Shortcut to Zeno with Action-Based Scheduling Optimization

The quantum Zeno effect asserts that quantum measurements inhibit simultaneous unitary dynamics when the “collapse” events are sufficiently strong and frequent. This applies in the limit of strong continuous measurement or dissipation. It is possible to implement a dissipative control that is known as “Zeno dragging” by dynamically varying the monitored observable, and hence also the eigenstates, which are attractors under Zeno dynamics. This is similar to adiabatic processes, in that the Zeno-dragging fidelity is highest when the rate of eigenstate change is slow compared to the measurement rate. We demonstrate here two theoretical methods for using such dynamics to achieve control of quantum systems. The first, which we shall refer to as “shortcut to Zeno,” is analogous to the shortcuts to adiabaticity (counterdiabatic driving) that are frequently used to accelerate unitary adiabatic evolution. In the second approach, we apply the Chantasri-Dressel-Jordan stochastic action [PRA 88, 042110 (2013)], and demonstrate that the extremal-probability readout paths derived from this are well suited to setting up a Pontryagin-style optimization of the Zeno-dragging schedule. A fundamental contribution of the latter approach is to show that an action suitable for measurement-driven control optimization can be derived quite generally from statistical arguments. Implementing these methods on the Zeno dragging of a qubit, we find that both approaches yield the same solution, namely, that the optimal control is a unitary that matches the motion of the Zeno-monitored eigenstate. We then show that such a solution can be more robust than a unitary-only operation and we comment on solvable generalizations of our qubit example embedded in larger systems. These methods open up new pathways toward systematically developing dynamic control of Zeno subspaces to realize dissipatively stabilized quantum operations. Published by the American Physical Society 2024

Physics↗
DOE OSTI2024

AOI [1] Advanced Manufacturing of Ceramic Anchors with Embedded Sensors for Process and Health Monitoring of Coal Boilers

Researchers at West Virginia University (WVU) developed methods to fabricate and test ceramic anchors with an embedded sensor technology for monitoring the health and processing conditions within pulverized coal (PC) and fluidized-bed combustion (FBC) boiler systems. The technology included the development of advanced manufacturing processes for 2D/3D printing electroceramic (conductive ceramic) sensor designs within the ceramic anchor microstructure during the manufacturing process. This advanced manufacturing process would allow for the precise control of local microstructure and composition in order to engineer layer-by-layer any protective and electrically active materials within the refractory anchor. This 3D printing technology would permit the rapid and controlled design of the refractory microstructure and embedded sensor design throughout the volume of the ceramic anchor. The work also included a method to interconnect the sensors to boiler shell through the anchor clamp, where the sensor signals will be processed by low-power electronics and transmitted wirelessly to a central processing hub. The end-goal of the program was to produce a ceramic anchor sensor system which would be ready for implementation within a coal boiler, and/or other similar refractory liner systems (such as that in the glass and metal manufacturing areas). The project objectives were to: 1) Define the chemical and microstructural stability, in addition to the electrical properties, of oxide and non-oxide ceramic composites to be embedded within the ceramic anchor compositions that may operate up to 1400ºC; 2) Develop and implement the 2D/3D printing technology to pattern and control the microstructure of the ceramic anchor and embedded sensor circuits; 3) Develop an interconnect technology which will permit easy installation of the ceramic anchors and signal collection at the boiler shell; 4) Develop low power analog electronics and wireless communication hardware to efficiently collect the sensor signal at each processing unit and transmit data to a central hub for data analysis; 5) Demonstrate the smart ceramic anchor system for temperature and liner fracture within a high-temperature processing unit, such as a boiler furnace or glass melting furnace floor/wall liner.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Exponential concentration in quantum kernel methods

Kernel methods in Quantum Machine Learning (QML) have recently gained significant attention as a potential candidate for achieving a quantum advantage in data analysis. Among other attractive properties, when training a kernel-based model one is guaranteed to find the optimal model’s parameters due to the convexity of the training landscape. However, this is based on the assumption that the quantum kernel can be efficiently obtained from quantum hardware. In this work we study the performance of quantum kernel models from the perspective of the resources needed to accurately estimate kernel values. We show that, under certain conditions, values of quantum kernels over different input data can be exponentially concentrated (in the number of qubits) towards some fixed value. Thus on training with a polynomial number of measurements, one ends up with a trivial model where the predictions on unseen inputs are independent of the input data. We identify four sources that can lead to concentration including expressivity of data embedding, global measurements, entanglement and noise. For each source, an associated concentration bound of quantum kernels is analytically derived. Lastly, we show that when dealing with classical data, training a parametrized data embedding with a kernel alignment method is also susceptible to exponential concentration. Our results are verified through numerical simulations for several QML tasks. Altogether, we provide guidelines indicating that certain features should be avoided to ensure the efficient evaluation of quantum kernels and so the performance of quantum kernel methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Heliostat sizing methodology for concentrating solar thermal industrial process heat projects

This study presents a method to obtain a heliostat size that minimizes the levelized cost of heat (LCOH) of a heliostat-based concentrating solar thermal system for applications of solar heating for industrial processes at operating temperatures from 565 to 1550°C. The method extends prior work by embedding a routine for system design that obtains near-optimal subsystem sizes, increasing the fidelity of drive cost functions, and adding an optical performance model to supplement the previously developed cost models, which we update to reflect current pricing trends. An illustrative business case is developed for Daggett, California, targeting specified annual thermal energy outputs of 50 to 400 GWh th . Optical performance is modeled using verified estimates from the literature. A surrogate heliostat cost model, derived from commercial heliostat designs and scaled for production volume, installation, and operations and maintenance costs, is used to develop cost functions. Results show that heliostat size strongly affects the LCOH, producing a characteristic U-shaped trend with a robust near-optimal window of 7-20 m 2 ; the heliostat size producing the lowest project cost in our study grows slightly as the project size increases, and is reduced as the operating temperature increases. The findings in this study are consistent with the general trend of smaller heliostats being deployed at existing projects for high-temperature industrial process heat and reflect the significant reduction in power electronics and other per-heliostat costs. The methodology we propose is general and can be tailored to revised cost curves as the technology continues to evolve.

14 SOLAR ENERGY↗