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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dendritic Computing with Multigate Ferroelectric Field-Effect Transistors

Although inspired by neuronal systems in the brain, artificial neural networks generally employ point-neurons, which offer computational complexity far less than that of their biological counterparts. Neurons have dendritic arbors that connect to different sets of synapses and offer local nonlinear accumulation – playing a pivotal role in processing and learning. Inspired by this, we propose a novel neuron design based on a multigate ferroelectric field-effect transistor that mimics dendrites. It leverages ferroelectric nonlinearity for local computations within dendritic branches while utilizing the transistor action to generate the neuronal output. The branched architecture enables smaller crossbar arrays in hardware integration, improving efficiency. Using an experimentally calibrated device-circuit-algorithm cosimulation framework, we demonstrate that networks incorporating our dendritic neurons achieve superior performance compared to much larger networks without dendrites (∼ 17× fewer trainable weight parameters). These findings suggest that dendritic hardware can significantly improve computational efficiency and learning capacity of neuromorphic systems optimized for edge applications.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Characterizing Technetium in Subsurface Sediments for Contaminant Remediation

Technetium-99 (Tc) contamination remains a major environmental problem at legacy nuclear reprocessing sites, including the Hanford Site (Washington State, USA) where ~700 Ci of Tc has been released into the subsurface. The environmental risk associated with Tc is due to its long half-life (213,000 years) and high mobility of the oxidized anionic pertechnetate species, Tc(VII)O 4 - , which warranted the US Department of Energy to identify Tc as a primary contaminant of concern. This risk generates a critical need to resolve scientific and technical issues underpinning the ability to safely, cost-effectively, and efficiently remediate Tc-contaminated vadose zone sediments and groundwater and mitigate risk to human health and the environment. Developing enhanced attenuation and efficient remediation strategies for released Tc requires a complete understanding of retardation processes and Tc mass flux, including the different mechanisms by which Tc is immobilized in the subsurface and the effect of localized subsurface conditions. Selection of over 30 sediments from Hanford waste disposal sites, based on historical information and sediment characterization, for analysis by autoradiography revealed that Tc concentrations were generally below the detection limit of 5 mg Tc/g sediment. When Tc was measurable in vadose zone sediments, it was predominantly present as TcO 4 - in water films associated with fine-grained sediments, with a maximum of 12 % of the total Tc present in the acid-extractable fraction, defined here as the immobile fraction. However, beneath one waste disposal site, where sediments containing minerals with reducing capacity intercepted miscellaneous fission product recovery waste and waste from the bismuth phosphate process, the amount of Tc present in the immobile fraction was 53 % of the total. Characterization of Tc-containing phases present in these field-contaminated sediments for the first time using Tc K-edge X-ray absorption near edge structure spectroscopy revealed that, as well as Tc present as Tc(VII)O 4 - in pore water associated with fine-grained sediments, Tc was also: (i) physically encapsulated within solid phases precipitated from other waste components and in multi-component phosphate minerals; and (ii) present as mixed Tc(VII)/Tc(IV) in localized reducing zones. These results will be used to develop improved long-term Tc remediation strategies optimized for field application, through stimulation of conditions that enhance Tc attenuation.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Probing the Impact of Solvation on Photoexcited Spin Crossover Complexes with High-Precision X-ray Transient Absorption Spectroscopy

Investigating the photoinduced electronic and structural response of bistable molecular building blocks incorporating transition metals in solution phase constitutes a necessary stepping stone for steering their properties towards applications and perfomance optimizations. Here, this paper presents a detailed X-ray transient absorption (XTA) spectroscopy study of a prototypical spin crossover (SCO) complex [Fe II (mbpy) 3 ] 2+ (where mbpy=4,4’-dimethyl-2,2’-bipyridine) with a [Fe II N 6 ] first coordination shell in water (H 2 O) and acetonitrile (CH 3 CN). The unprecedented data quality of the XTA spectra together with the direct fitting of the difference spectra in k space using a large number of scattering paths enables resolving the subtle difference in the photoexcited structures of an Fe II complex in two solvents for the first time. Also, compared to the low spin (LS) 1 A 1 state, the average Fe-N bond elongations for the photoinduced high spin (HS) 5 T 2 state are found to be 0.181 ± 0.003 Å in H 2 O and 0.199 ± 0.003 Å in CH 3 CN. This difference in structural response is attributed to ligand-solvent interactions that are stronger in H 2 O than in CH 3 CN for the HS excited state. Our studies demonstrate that, although the metal center of [Fe II (mbpy) 3 ] 2+ could have been expected to be rather shielded by the three bidentate ligands with quasi-octahedral-coordination, the ligand field strength in the HS excited state is nevertheless indirectly affected by solvation that modifies the charge distribution within the Fe-N covalent bonds. More generally, this work highlights the importance of including solvation effects in order to develop a generalized understanding of the spin-state switching at the atomic level.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Enhancing the efficiency of time-dependent density functional theory calculations of dynamic response properties

X-ray Thomson scattering (XRTS) constitutes an essential technique for diagnosing material properties under extreme conditions, such as high pressures and intense laser heating. Time-dependent density functional theory (TDDFT) is one of the most accurate available ab initio methods for modeling XRTS spectra, as well as a host of other dynamic material properties. However, strong thermal excitations, along with the need to account for variations in temperature and density as well as the finite size of the detector significantly increase the computational cost of TDDFT simulations compared to ambient conditions. In this work, we present a broadly applicable method for optimizing and enhancing the efficiency of TDDFT calculations. Our approach is based on a one-to-one mapping between the dynamic structure factor and the imaginary time density–density correlation function, which naturally emerges in Feynman’s path integral formulation of quantum many-body theory. Specifically, we combine rigorous convergence tests in the imaginary time domain with a constraints-based attenuation of narrow-band fluctuations to improve the efficiency of TDDFT modeling without the introduction of any significant bias. As a result, we can report a speed-up by up to an order of magnitude, thus substantially reducing the burden of computational cost required for XRTS analysis.

Moldabekov, Zhandos A. [Helmholtz-Zentrum Dresden-↗
DOE OSTI2024

Discovery of potent small-molecule inhibitors of lipoprotein(a) formation

Lipoprotein(a) (Lp(a)), an independent, causal cardiovascular risk factor, is a lipoprotein particle that is formed by the interaction of a low-density lipoprotein (LDL) particle and apolipoprotein(a) (apo(a)). Apo(a) first binds to lysine residues of apolipoprotein B-100 (apoB-100) on LDL through the Kringle IV (K IV ) 7 and 8 domains, before a disulfide bond forms between apo(a) and apoB-100 to create Lp(a). Here we show that the first step of Lp(a) formation can be inhibited through small-molecule interactions with apo(a) K IV 7–8. We identify compounds that bind to apo(a) K IV 7–8, and, through chemical optimization and further application of multivalency, we create compounds with subnanomolar potency that inhibit the formation of Lp(a). Oral doses of prototype compounds and a potent, multivalent disruptor, LY3473329 (muvalaplin), reduced the levels of Lp(a) in transgenic mice and in cynomolgus monkeys. Although multivalent molecules bind to the Kringle domains of rat plasminogen and reduce plasmin activity, species-selective differences in plasminogen sequences suggest that inhibitor molecules will reduce the levels of Lp(a), but not those of plasminogen, in humans. These data support the clinical development of LY3473329—which is already in phase 2 studies—as a potent and specific orally administered agent for reducing the levels of Lp(a).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Multi-fidelity information fusion with concatenated neural networks

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.

42 ENGINEERING↗
DOE OSTI2018

Overcoming ammonia synthesis scaling relations with plasma-enabled catalysis

Correlations between the energies of elementary steps limit the rates of thermally catalysed reactions at surfaces. Here, we show how these limitations can be circumvented in ammonia synthesis by coupling catalysts to a non-thermal plasma. We postulate that plasma-induced vibrational excitations in N 2 decrease dissociation barriers without influencing subsequent reaction steps. We develop a density-functional-theory-based microkinetic model to incorporate this effect, and parameterize the model using N 2 vibrational excitations observed in a dielectric-barrier-discharge plasma. We predict plasma enhancement to be particularly great on metals that bind nitrogen too weakly to be active thermally. Ammonia synthesis rates observed in a dielectric-barrier-discharge plasma reactor are consistent with predicted enhancements and predicted changes in the optimal metal catalyst. The results provide guidance for optimizing catalysts for application with plasmas.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Foundations and strategies of the construction of hybrid catalysts for optimized performances

Catalysts are generally classified into three categories: homogeneous, heterogeneous and enzyme, each evolved as an independent field. Efforts to bridge these fields are scarce but desirable. In this paper, we first describe how numerous classes of reactions can be achieved by all three categories of catalysts. Examples are given based on a selective survey of the literature. Next, a selection of important approaches, the benefits and challenges of constructing heterogeneous–homogeneous, heterogeneous–enzyme and homogeneous–enzyme hybrid catalysts are discussed based on published researches. Hybrid catalysts not only increase the performance, including activity, selectivity, lifetime and recyclability compared to one of the components, but also offer extra functions such as a microenvironment for different reaction pathways, and cascade catalysis for products that are challenging to produce. Finally, we expect future tailor-made hybrid catalysts will combine the advantages of the components and be optimized for industrial applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Band gap predictions of double perovskite oxides using machine learning

Abstract The compositional and structural variety inherent to oxide perovskites spawn wide-ranging applications. In perovskites, the band gap E g , a key material parameter for these applications, can be optimally controlled by varying the composition. Here, we implement a hierarchical screening process in which two cross-validated and predictive machine learning models for band gap classification and regression, trained using exhaustive datasets that span 68 elements of the periodic table, are applied sequentially. The classification model separates wide band gap materials, with E g ≥ 0.5 eV, from materials which have zero or relatively small band gaps, namely E g < 0.5 eV, and the second regression model quantitatively predicts the gap value of the wide band gap compounds. The study down-selects 13,589 cubic oxide perovskite compositions that are predicted to be experimentally formable, thermodynamically stable, and have a wide band gap. Of these, a subset of 310 compounds, which are predicted to be stable and formable with a confidence greater than 90%, are identified for further investigation. Our models are methodically analyzed via performance metrics and inter-dependence of model features to gain physical insight into the band gap prediction problem. Design maps to identify the variation of band gap with substitution of different elements are also presented.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Multi-Modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation

As data collection and simulation capabilities advance, multi-modal learning, the task of learning from multiple modalities and sources of data, is becoming an increasingly important area of research. Surrogate models that learn from data of multiple auxiliary modalities to support the modeling of a highly expensive quantity of interest have the potential to aid outer loop applications such as optimization, inverse problems, or sensitivity analyses when multi-modal data are available. We develop two multi-modal Bayesian neural network surrogate models and leverage conditionally conjugate distributions in the last layer to estimate model parameters using stochastic variational inference (SVI). We provide a method to perform this conjugate SVI estimation in the presence of partially missing observations. Here, we demonstrate improved prediction accuracy and uncertainty quantification compared to unimodal surrogate models for both scalar and time series data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Impurity transport studies at the HSX stellarator using active and passive CVI spectroscopy

The transport of carbon impurities has been studied in the helically symmetric stellarator experiment (HSX) using active and passive charge exchange recombination spectroscopy (CHERS). For the analysis of the CHERS signals, the STRAHL impurity transport code has been re-written in the python programming language and optimized for the application in stellarators. In addition, neutral hydrogen densities both along the NBI line of sight as well as for the background plasma have been calculated using the FIDASIM code. By using the basinhopping algorithm to minimize the difference between experimental and predicted active and passive signals, significant levels of impurity diffusion are observed. In this work, comparisons with neoclassical calculations from DKES/PENTA show that the inferred levels exceed the neoclassical transport by about a factor of four in the core and more than 100 times towards the plasma edge, thus indicating a high level of anomalous transport. This observation is in agreement with experimental heat diffusivites determined from a power balance analysis which exhibits strong anomalous transport as well.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Towards a quantum realization of the ampere using single-electron resolution Skipper-CCDs

This paper presents a proof-of-concept demonstration of the Skipper-charge coupled device (CCD), a sensor with single-electron counting capability, as a promising technology for implementing an electron-pump-based current source. Relying on its single-electron resolution and built-in charge sensing, it allows self-calibration of the charge packets. This article presents an initial discussion of the uncertainty in the charge measurement and achievable currents levels with this technology. Here, we report experimental results that illustrate the key functionalities in manipulating both small and large electron charge packets, including a comparison of the charge generated, self-measured, and drained by the sensor against measurements from an electrometer. These results were obtained using a standard sensor and readout electronics without specific optimizations for this application. The objective is to explore the potential of Skipper-CCD for realizing an electron-based current source.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Characterization and Optimization of the Fitting of Quantum Correlation Functions

This case study presents a characterization and optimization of an application code for extracting parton distribution functions from high energy electron-proton scattering data. Profiling this application code reveals that the phase-space density computation accounts for 93% of the overall execution time for a single iteration on a single core. When executing multiple iterations in parallel on a multicore system, the application spends 78% of its overall execution time idling due to load imbalance. We address these issues by first transforming the application code from Python to C++ and then tackling the application load imbalance via a hybrid scheduling strategy that combines dynamic and static scheduling. These techniques result in a 62% reduction in CPU idle time and a 2.46x speedup in overall execution time per node. In addition, the typically enabled power-management mechanisms in supercomputers (e.g., AMD Turbo Core, Intel Turbo Boost, and RAPL) can significantly impact intra-node scalability when more than 50% of the CPU cores are used. This finding underscores the importance of understanding system interactions with power management, as they can adversely impact application performance, and highlights the necessity of intra-node scaling tests to identify performance degradation that inter-node scaling tests might otherwise overlook.

Chuang, Pi-Yueh [Virginia Tech,Dept. of Computer S↗
DOE OSTI2024

Evaluation of GlassNet for physics-informed machine learning of glass stability and glass-forming ability

Glassy materials form the basis of many modern applications, including nuclear waste immobilization, touch-screen displays, and optical fibers, and also hold great potential for future medical and environmental applications. However, their structural complexity and large composition space make design and optimization challenging for certain applications. Of particular importance for glass processing and design is an estimate of a given composition's glass-forming ability (GFA). However, there remain many open questions regarding the underlying physical mechanisms of glass formation, especially in oxide glasses. It is apparent that a proxy for GFA would be highly useful in glass processing and design, but identifying such a surrogate property has proven itself to be difficult. While glass stability (GS) parameters have historically been used as a GFA surrogate, recent research has demonstrated that most of these parameters are not accurate predictors of the GFA of oxide glasses. Here, in this work, we explore the application of an open-source pre-trained neural network model, GlassNet, that can predict the characteristic temperatures necessary to compute GS with reasonable performance and assess the feasibility of using these physics-informed machine learning (PIML)-predicted GS parameters to estimate GFA. In doing so, we track the uncertainties at each step of the computation—from the original ML prediction errors to the compounding of errors during GS estimation, and finally to the final estimation of GFA. While GlassNet exhibits reasonable accuracy on all individual properties, we observe a large compounding of error in the combination of these individual predictions for the PIML prediction of GS, finding that random forest models offer similar accuracy to GlassNet. We also break down the performance of GlassNet on different glass families and find that the error in GS prediction is correlated with the error in crystallization peak temperature prediction. Lastly, we utilize this finding to assess the relationship between top-performing GS parameters and GFA for two ternary glass systems: sodium borosilicate and sodium iron phosphate glasses. We conclude that to obtain true ML predictive capability of GFA, significantly more data needs to be collected.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Partitioned Coupling for Structural Acoustics

Here, we expand the second-order fluid–structure coupling scheme of Farhat et al. (1998, “Load and Motion Transfer Algorithms for 19 Fluid/Structure Interaction Problems With Non-Matching Discrete Interfaces: Momentum and Energy Conservation, Optimal Discretization and Application to Aeroelasticity,” Comput. Methods Appl. Mech. Eng., 157(1–2), pp. 95–114; 2006, “Provably Second-Order Time-Accurate Loosely-Coupled Solution Algorithms for Transient Nonlinear Computational Aeroelasticity,” Comput. Methods Appl. Mech. Eng., 195(17), pp. 1973–2001) to structural acoustics. The staggered structural acoustics solution method is demonstrated to be second-order accurate in time, and numerical results are compared to a monolithically coupled system. The partitioned coupling method is implemented in the Sierra Mechanics software suite, allowing for the loose coupling of time domain acoustics in SIERRA/SD to structural dynamics ( SIERRA/SD ) or solid mechanics ( SIERRA/SD ). The coupling is demonstrated to work for nonconforming meshes. Results are verified for a one-dimensional piston, and the staggered and monolithic results are compared to an exact solution. Huang, H. (1969, “Transient Interaction of Plane Acoustic Waves With a Spherical Elastic Shell,” J. Acoust. Soc. Am., 45(3), pp. 661–670) sphere scattering problem with a spherically spreading acoustic load demonstrates parallel capability on a complex problem. Our numerical results compare well for a bronze plate submerged in water and sinusoidally excited (Fahnline and Shepherd, 2017, “Transient Finite Element/Equivalent Sources Using Direct Coupling and Treating the Acoustic Coupling Matrix as Sparse,” J. Acoust. Soc. Am., 142(2), pp. 1011–1024).

42 ENGINEERING↗
DOE OSTI2023

High-throughput identification of spin-photon interfaces in silicon

Color centers in host semiconductors are prime candidates as spin-photon interfaces for quantum applications. Finding an optimal spin-photon interface in silicon would move quantum information technologies toward a mature semiconducting host. However, the space of possible charged defects is vast, making the identification of candidates from experiments alone extremely challenging. Here, we use high-throughput first-principles computational screening to identify spin-photon interfaces among more than 1000 charged defects in silicon. The use of a single-shot hybrid functional approach is critical in enabling the screening of many quantum defects with a reasonable accuracy. We identify three promising spin-photon interfaces as potential bright emitters in the telecom band: Ti$^+_i$, Fe$^0_i$, and Ru$^0_i$. These candidates are excited through defect-bound excitons, stressing the importance of such defects in silicon for telecom band operations. Our work paves the way to further large-scale computational screening for quantum defects in semiconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Affine Approximation of Parametrized Kernels and Model Order Reduction for Nonlocal and Fractional Laplace Models

In this work, we consider parametrized problems driven by spatially nonlocal integral operators with parameter-dependent kernels. In particular, kernels with varying nonlocal interaction radius $\delta > 0$ and fractional Laplace kernels, parametrized by the fractional power $s\in(0,1)$, are studied. Furthermore, in order to provide an efficient and reliable approximation of the solution for different values of the parameters, we develop the reduced basis method as a parametric model order reduction approach. Major difficulties arise since the kernels are not affine in the parameters, singular, and discontinuous. Moreover, the spatial regularity of the solutions depends on the varying fractional power $s$. To address this, we derive regularity and differentiability results with respect to $\delta$ and $s$, which are of independent interest for other applications such as optimization and parameter identification. We then use these results to construct affine approximations of the kernels by local polynomials. Finally, we certify the method by providing reliable a posteriori error estimators, which account for all approximation errors, and support the theoretical findings by numerical experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

An Efficient Electrochemical Tanks-in-Series Model for Lithium Sulfur Batteries

This article applies and efficiently implements the Tanks-in-Series methodology ( J. Electrochem. Soc. , 167 , 013534 (2020)) to generate a computationally efficient electrochemical model for Lithium-Sulfur batteries. The original Tank model approach for Lithium-ion batteries is modified to account for porosity changes with time. In addition, an exponential scaling method is introduced that enables efficient simulation of the model equations to address the wide range of time constants present for different reactions in the Lithium-Sulfur system. The Tank Model achieves acceptable voltage error even for transport-limited discharged conditions. Predictions of internal electrochemical variables are examined, and electrochemical implications of the approximations discussed. This suggests significant potential for real-time applications such as optimal charging, cell-balancing, and estimation, and represents a step forward in efforts to incorporate detailed electrochemical models in advanced Battery Management Systems for Lithium-Sulfur batteries.

Parke, Caitlin D. (ORCID:0000000177324044)↗