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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Intern Poster

Digital Image Correlation (DIC) is an optical technique that combines image registration and tracking methods for accurate two-dimensional and three-dimensional changes in images. DIC software can be used to track the contour, deformation, and strain of a sample. In the Advanced Test Reactor (ATR) at INL (Idaho National Laboratory) there exists a small working window of samples that can become irradiated. Hundreds of graphite disks called piggybacks have undergone this irradiation as part of the Advanced Reactor Technologies (ART) program. After irradiation, it is desirable to understand the change in tensile strength as a function of dose. Due to the limited space in the ATR, typical dog bone tensile tests reduce the number of graphite samples from hundreds to tens. However, there does exist an ASTM standard, D8289, which uses disc compression of graphite to estimate the tensile strength of the specimen with the Brazilian Disk test fixture. While only used as an estimate, which is typically off by a third, it is the purpose of this study to identify how to amend D8289 to remove the word "estimate" with the help of DIC.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Bayesian Optimization for Reactor Design Optimization

This study present a test case in which the Bayesian Optimization method is applied to a simulation-based reactor core design optimization problem. The test case aims to showcase the potential of an automated design optimization algorithm for reactor designs by streamlining the reactor core design workflow, given the high computational cost of simulations. The contributions of this work are threefold. First, the existing HTGR model is converted into a simulation-based design optimization test case by developing a pipeline that enables modification of key design parameters and evaluates design performance based on simulation outputs. Second, Bayesian Optimization is implemented and adapted to demonstrate the feasibility of automatic design optimization for nuclear reactor core. Proposed approach leverages Gaussian Process models to characterize the relationship between design variables and performance metrics, while incorporating novel acquisition functions that balance exploration of the design space with exploitation of promising configurations. This implementation lays the foundation for the future developments of reactor design optimization algorithms.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

High-throughput reaction discovery for Cs–Pb–Br nanocrystal synthesis

High-throughput reaction discovery is necessary to understand complex reaction spaces for inorganic nanocrystal synthesis. Here, we implemented a high-throughput continuous flow millifluidic reactor to perform reaction discovery for Cs–Pb–Br nanocrystal synthesis using a ligand assisted reprecipitation (LARP)-type approach. 3D-printed flow resistors enable the screening of up to 16 different mixing ratios within a single 90 s run, allowing for >270 different precursor concentration ratios to be quickly tested to explore the phase space that results in CsPbBr 3 , Cs 4 PbBr 6 , a biphasic mixture, or no product. To construct a full phase map from these high-throughput experiments, a neural network was trained and validated to predict the product composition (~500 000 points in precursor concentration space). The phase map predicts product composition/phase as a function of Cs–Pb–Br feed ratio. As a result, this approach demonstrates how high-throughput flow chemistry can be used in tandem with machine learning to rapidly explore nanocrystal reaction spaces in flow.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Z-Target Radiography Postprocessing With A Deep Convolution Neural Network

Analyzing X-ray radiographs is crucial for understanding target behavior in Inertial Confinement Fusion (ICF) and High Energy Density (HED) platforms. However, the density of Magneto Raleigh Taylor (MRT) bands and limitations of target materials often obscure relevant spike growth and density information. To address this issue, machine learning postprocessing techniques can be applied to remove darkened regions in radiography images. In this study, a novel method is presented for removing MRT darkened regions from z-target radiographs using a convolutional neural network (CNN). The CNN, consisting of six layers, treats the darkened regions as noise and employs a mixed loss function and end-to-end frameworks to suppress them while preserving sharpness. The six-layer architecture is designed to effectively learn features when provided with a larger volume of learning space. Each layer is optimized using a mixed loss function that combines a standard loss pixel approach with a multi-scaled structural similarity index loss, which considers luminance, contrast, and structure in local neighborhoods. This approach is particularly beneficial for capturing the stochastic structure of MRT limbs. Due to the limited availability of experimental data, training is conducted using synthetic target radiography from 3D Alegra simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Distributed Quantum-Enhanced Optimization: A Topographical Preconditioning Approach for High-Dimensional Search

Optimization problems become fundamentally challenging as the number of variables increases. Because the volume of the search space grows exponentially, classical algorithms frequently fail to locate the global minimum of non-convex functions. While quantum optimization offers a potential alternative, mapping continuous problems onto near-term quantum hardware introduces severe scaling limits and barren plateaus. To bridge this gap, we propose the Distributed Quantum-Enhanced Optimization (D-QEO) framework. Instead of forcing the quantum processor to find the exact minimum, we use it simply as a topographical preconditioner. The QPU maps the landscape to locate the most promising basin of attraction, generating high-quality seed points for a classical GPU-accelerated solver to refine. To make this approach viable for utility-scale problems, we exploit the mathematical structure of separable functions. This allows us to cut a 50-qubit (i.e., $2^{50}$) global search space into independent and manageable sub-spaces using 5-qubit subcircuits. By executing these fragments concurrently with CUDA-Q, we completely bypass the overhead of cross-register entanglement and classical tensor knitting for separable functions. Benchmarks on the 10-dimensional Rastrigin and Ackley functions show that D-QEO prevents the exponential failure rates observed in purely classical algorithms. Furthermore, this quantum warm-start significantly reduces the number of classical BFGS iterations required to converge, providing a highly practical blueprint for utilizing near-term quantum resources in complex global search.

Soos, Dominik [Old Dominion U.]↗
DOE OSTI2026

Finite-range pairing in nuclear density functional theory

Pairing correlations are ubiquitous in low-energy states of atomic nuclei. To incorporate them within nuclear density functional theory, often used for global computations of nuclear properties, pairing functionals that generate nucleonic pair densities and pairing fields are introduced. Many pairing functionals currently used can be traced back to zero-range nucleon-nucleon interactions. Unfortunately, such functionals are plagued by deficiencies that become apparent in large model spaces that contain unbound single-particle (continuum) states. In particular, the underlying computational schemes diverge as the single-particle space increases, and the results depend on how marginally occupied states are incorporated. These problems become more pronounced for pairing functionals that contain gradient-density dependence, such as in the Fayans functional. To remedy this, finite-range pairing functionals are introduced. In this study, this is done by folding the pair density with Gaussians. Here, we show that a folding radius of about 1 fm offers the best compromise between quality and stability, and substantially reduces the pathological behavior in different numerical applications.

Nuclear density functional theory↗
DOE OSTI2025

Fluoropolymer Composites from Partially Perfluoroalkylated Waste Polyethylene

Chemically modified plastics have emerged as practical solutions to plastic waste increases. Here, the inherent novelty of decorating polymer chains with chemical functionality results in distinct properties that expand the available application space. Nevertheless, developing designer materials for specific applications beyond compatibilization or mild property enhancement is difficult due to the synergistic effects of both the polar functionality imparted and the parent materials' intrinsic properties. By incorporating perfluoro-alkyl side-chains onto the backbone of dehydrogenated waste HDPE, unique surface properties intermediate between polytetrafluoroethylene (PTFE, the model fluoropolymer) and HDPE become apparent, while the overall material mechanical and thermal properties result in more LLDPE-like materials. This is demonstrated through moderate decreases in the surface free energy of the perfluoroalkylated polyolefin surface (increase in H 2 O contact angle of ~ 6°) and increased ordering under shear when blended with PTFE nanoparticles where the crossover point occurred at higher strains. Critically, perfluoroalkylated HDPE possesses improved rheological modification properties at elevated temperatures with PTFE nanoparticles, resulting in more thermally robust and stable composite materials.

fluoropolymer↗
DOE OSTI2024

Logistic function as a characteristic of multipactor development

Simulations of multipacting with or without space charge effect bring out a different behavior of particle number growth, namely, the exponential growth of particle number in the simulations without space charge effect and the saturation of particle number (or collision and emission currents) when space charge is considered. That creates a certain confusion in evaluation and comparison of overall danger of multipactor between the approaches. On the other hand, both growth rate and total multipactor current loading at saturation are important for multipactor barriers evaluation. It was noticed and then verified that the logistic function, widely used in chemistry, biology, and ecosystem study, reproduces the particle number growth curves remarkably well. The function contains the parameters, which can be interpreted as particle number growth rate and multipactor current saturation level, so both become correlated and obtained simultaneously in one run. In this work it is shown how the logistic function can be used for characterization of the multipactor barriers and how it can be used for possible reduction of simulation time in the simulations with space charge effect.

Romanov, Gennady↗
DOE OSTI2025

Transverse-momentum-dependent pion structures from lattice QCD: Collins-Soper kernel, soft factor, TMDWF, and TMDPDF

We present the first lattice quantum chromodynamics (QCD) calculation of the pion valence-quark transverse-momentum-dependent parton distribution function (TMDPDF) within the framework of large-momentum effective theory (LaMET). Using correlators fixed in the Coulomb gauge (CG), we computed the quasi-TMD beam function for a pion with a mass of 300 MeV, a fine lattice spacing of 𝑎 =0.06 fm, and multiple large momenta up to 3 GeV. The intrinsic soft functions in the CG approach are extracted from form factors with large momentum transfer, and as a byproduct, we also obtain the corresponding Collins-Soper (CS) kernel. Our determinations of both the soft function and the CS kernel agree with perturbation theory at small transverse separations (𝑏 ⊥ ) between the quarks. At larger 𝑏 ⊥ , the CS kernel remains consistent with recent results obtained using both CG and gauge-invariant TMD correlators in the literature. By combining next-to-leading logarithmic factorization of the quasi-TMD beam function and the soft function, we obtain an 𝑥-dependent pion valence-quark TMDPDF for transverse separations 𝑏 ⊥ ≳1 fm. Interestingly, we find that the 𝑏 ⊥ dependence of the phenomenological parametrizations of TMDPDF for moderate values of 𝑥 are in reasonable agreement with our QCD determinations. In addition, we present results for the transverse-momentum-dependent wave function for a heavier pion with 670 MeV mass.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Efficient Parameterization of Density Functional Tight-Binding for 5 f -Elements: A Th–O Case Study

Density functional tight binding (DFTB) models for f-element species are challenging to parametrize owing to the large number of adjustable parameters. The explicit optimization of the terms entering the semiempirical DFTB Hamiltonian related to f orbitals is crucial to generating a reliable parametrization for f-block elements, because they play import roles in bonding interactions. However, since the number of parameters grows quadratically with the number of orbitals, the computational cost for parameter optimization is much more expensive for the f-elements than for the main group elements. In this work we present a set of efficient approaches for mitigating the hurdle imposed by the large size of the parameter space. A novel group-by-orbital correction functions for two-center bond integrals was developed. With this approach the number of parameters is reduced, and it grows linearly with the number of elements, maintaining the accuracy and the number of parameters, in the case of f elements, by more than 40%. The parameter optimization step was accelerated by means of the mini-batch BFGS method. This method allows parameter optimizations with much larger training sets than other single batch methods. A stochastic optimizer was employed that helped overcome shallow local minima in the objective function. The proposed algorithm was used to parametrize the DFTB Hamiltonian for the Th–O system, which was subsequently applied to the study of ThO 2 nanoparticles. The training set consisted of 6322 unique structures, which is barely feasible with conventional optimization methods. The optimized parameter set, LANL-ThO, displays good agreement with DFT-calculated properties such as energies, forces, and structures for both clusters and bulk ThO 2 . Benefiting from the fewer number of parameters and lower computational costs for objective function evaluations, this new approach shows its potential applications in DFTB parametrization for elements with high angular momentum, which present a challenge to conventional methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Hierarchical Gaussian process-based Bayesian optimization for materials discovery in high entropy alloy spaces

Bayesian optimization (BO) is a powerful and data-efficient method for iterative materials discovery and design, particularly valuable when prior knowledge is limited, underlying functional relationships are complex or unknown, and the cost of querying the materials space is significant. Traditional BO methodologies typically utilize conventional Gaussian Processes (cGPs) to model the relationships between material inputs and properties, as well as correlations within the input space. However, cGP-BO approaches often fall short in multi-objective optimization scenarios, where they are unable to fully exploit correlations between distinct material properties. Leveraging these correlations can significantly enhance the discovery process, as information about one property can inform and improve predictions about others. Here, this study addresses this limitation by employing advanced kernel structures to capture and model multi-dimensional property correlations through multi-task (MTGPs) or deep Gaussian Processes (DGPs), thus accelerating the discovery process. We demonstrate the effectiveness of MTGP-BO and DGP-BO in rapidly and robustly solving complex materials design challenges that occur within the context of complex multi-objective optimization over FCC FeCrNiCoCu high entropy alloy (HEA) spaces, where traditional cGP-BO approaches fail. Furthermore, we highlight how the differential costs associated with querying various material properties can be strategically leveraged to make the materials discovery process more cost-efficient.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Rb 4 CuSb 2 Cl 11 and Rb 2 In 0.91(0.2) Sb 0.09 Cl 5 ·H 2 O: Wide Band Gap 0D Metal Halide Semiconductors

Herein, we report the discovery, structural and photophysical characterization of a new zero-dimensional (0D) lead-free all-inorganic halide, Rb 4 CuSb 2 Cl 11 , which adopts a new structure type. Single-crystal X-ray diffraction (SCXRD) shows that the structure consists of isolated, distorted seesaw [SbCl 4 ] − and trigonal planar [CuCl 3 ] 2− units, separated by Rb + cations that provide charge balance. Optoelectronic measurements and density functional theory (DFT) calculations indicate an indirect band gap of 2.89 eV, making it a candidate for wide-bandgap optoelectronic applications. Electrical resistivity was measured at 1.29 × 10 10 Ω·cm, and the trap-state density (n trap ) was found to be 7.44 × 10 10 cm −3 . Attempts to synthesize substitution analogs of Rb 4 CuSb 2 Cl 11 led to the synthesis of Rb 2 In 0.91(0.2) Sb 0.09 Cl 5 ·H 2 O, which was erroneously reported as Rb 2 SbCl 5 O in a previous study. Rb 2 In 0.91(0.2) Sb 0.09 Cl 5 ·H 2 O adopts a vacancyordered perovskite structure and exhibits broad-band yellow emission under UV excitation. The measured photoluminescence quantum yield (PLQY) for Rb 2 In 0.91 (0.2)Sb 0.09 Cl 5 ·H 2 O is 18.2%. These findings add to the growing class of quaternary metal halides with multiple cation and anion compositions, expanding the chemical phase space for the discovery of new materials with functional properties.

Crystal structure↗
DOE OSTI2024

Development and field demonstration of residential air source integrated heat pump using a three-stage compressor

To promote decarbonization and all electrification at residential sectors, it is necessary to use air source heat pumps (ASHPs) to replace natural gas for space heating and water heating. ASHPs are widely utilized for residential space cooling, heating, and water heating due to their simplicity and cost-effectiveness. However, their performance can be compromised in cold climates, where they may experience reduced heating capacity. A multi-functional heat pump, using a single compressor, to meet all home space conditioning and water heating demands, is an emerging technology. To address this limitation, we have developed and demonstrated an air source integrated heat pump to fulfill comprehensive home comfort requirements. This system employs a three-stage compressor and a single set of heat exchangers and valves, optimizing functionality while minimizing costs. The performance of the developed system was rigorously evaluated in both laboratory and field settings. In laboratory conditions, the system achieved a Seasonal Energy Efficiency Ratio of 17.0 (average COP of 4.98) and a Heating Seasonal Performance Factor of 11.0 (3.22). Additionally, in its most efficient operational mode—combining space cooling and water heating—the unit attained a total energy efficiency exceeding 7.0 seasonal COP in the field and could heat a 189-liter tank of water in just 25 min. The field study corroborated the laboratory findings, validating the system’s performance in real-world conditions. Here, this integrated heat pump represents an ideal solution for decarbonizing homes in northern climates by providing efficient space heating and water heating, thereby replacing the need for natural gas.

Integrated heat pump↗
DOE OSTI2024

Tailoring Molecular Space to Navigate Phase Complexity in Cs-Based Quasi-2D Perovskites via Gated-Gaussian-Driven High-Throughput Discovery

Cesium-based quasi-2D halide perovskites (HPs) offer promising functionalities and low-temperature manufacturability, suited to stable tandem photovoltaics. However, the chemical interplays between the molecular spacers and the inorganic building blocks during crystallization cause substantial phase complexities in the resulting matrices. To successfully optimize and implement the quasi-2D HP functionalities, a systematic understanding of spacer chemistry, along with the seamless navigation of the inherently discrete molecular space, is necessary. Herein, by utilizing high-throughput automated experimentation, the phase complexities in the molecular space of quasi-2D HPs are explored, thus identifying the chemical roles of the spacer cations on the synthesis and functionalities of the complex materials. Furthermore, a novel active machine learning algorithm leveraging a two-stage decision-making process, called gated Gaussian process Bayesian optimization is introduced, to navigate the discrete ternary chemical space defined with two distinctive spacer molecules. Through simultaneous optimization of photoluminescence intensity and stability that “tailors” the chemistry in the molecular space, a ternary-compositional quasi-2D HP film realizing excellent optoelectronic functionalities is demonstrated. Finally, this work not only provides a pathway for the rational and bespoke design of complex HP materials but also sets the stage for accelerated materials discovery in other multifunctional systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Toward machine-learning-assisted PW-class high-repetition-rate experiments with solid targets

We present progress in utilizing a machine learning (ML) assisted optimization framework to study the trends in a parameter space defined by spectrally shaped, high-intensity, petawatt-class (8 J, 45 fs) laser pulses interacting with solid targets and give the first simulation-based overview of predicted trends. A neural network (NN) incorporating uncertainty quantification is trained to predict the number of hot electrons generated by the laser–target interaction as a function of pulse shaping parameters. The predictions of this NN serve as the basis function for a Bayesian optimization framework to navigate this space. For post-experimental evaluation, we compare two separate neural network (NN) models. One is based solely on data from experiments, and the other is trained only on ensemble particle-in-cell simulations. Reviewing the predicted and observed trends across the experiment-capable laser parameter search space, we find that both ML models predict a maximal increase in hot electron generation at a level of approximately 12%–18%; however, no statistically significant enhancement was observed in experiments. On direct comparison of the NN models, the average discrepancy is 8.5%, with a maximum of 30%. Since shot-to-shot fluctuations in experiments affect the observations, we evaluate the behavior of our optimization framework by performing virtual experiments that vary the number of repeated observations and the noise levels. Here, we discuss the implications of such a framework for future autonomous exploration platforms in high-repetition-rate experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

High entropy oxides prediction and discovery by the Mixed Enthalpy-Entropy Descriptor

The vast, high-dimensional composition space of high-entropy oxides (HEOs) offers exceptional opportunities for functional materials discovery, yet it also poses a fundamental challenge: the rational and efficient prediction of stable, synthesizable compositions and the corresponding structure–property relationships. Despite growing interest, the field still lacks broadly applicable, physically grounded descriptors capable of navigating various large chemical spaces. Here, we introduce a Mixed Enthalpy–Entropy Descriptor (MEED) that enables rapid, first-principles–based prediction of HEOs synthesizability across diverse chemistries. Using MEED, we perform high-throughput screening of two distinct HEO families: rocksalt oxides and perovskite oxides. The predicted top candidates in each family were experimentally validated. MEED reveals unifying thermodynamic and structural principles governing stability across both chemical compositions and polymorphs, providing mechanistic insight into the formation of high-entropy phases. This work significantly broadens the accessible chemical design space for HEOs and establishes a data-efficient framework for accelerating the discovery of next-generation functional materials.

Yu, Liping [University of Central Florida]↗
DOE OSTI2026

Alternating salt and freshwater floods of coastal soils impact soil structure, hydraulic properties, and oxygen dynamics

Coastal soils are increasingly impacted by hydrologic intensification in the form of rising sea level and flooding from storm surges and precipitation. Alternating saltwater (SW) and freshwater (FW) exposure has the potential to disperse colloids, which can lead to disintegration of soil structure, clogging of pore spaces, and reduction in ecologically and biogeochemically important functions like infiltration or gas exchange. To investigate how hydrologic intensification affects soil structure and oxygen dynamics, we conducted a series of laboratory-based flood simulations. Intact soil cores of the A and B horizons (22 total) from the toe slope of an upland coastal forest along the western shore of Chesapeake Bay. We subjected the cores to 24 h of saturation with either FW or alternating brackish SW and FW, with a 24-h draining event in between flood events. Oxygen diffusion into soil during draining was reduced by up to 30% for soils flooded alternating SW-FW compared to soils flooded only with FW. We attributed the reduced oxygen diffusion to clogging of smaller pores by colloids, with colloid redistribution observed as an increase in specific surface area down the core profile of up to 59%. After three SW-FW floods (six floods total), there were significant changes in pore size distribution, significant redistribution of colloids, and the A horizon became sodic. We concluded that a small number of SW flooding events can induce a measurable change in soil physical properties that directly impacts the biogeochemical dynamics.

Rod, Kenton A. [Pacific Northwest National Laborat↗
DOE OSTI2024

Powder X-ray diffraction of nintedanib esylate hemihydrate, (C 31 H 33 N 5 O 4 )(C 2 H 5 O 3 S)(H 2 O) 0.5

The crystal structure of nintedanib esylate hemihydrate was refined using synchrotron X-ray powder diffraction data and optimized using density functional theory techniques. Nintedanib esylate hemihydrate crystallizes in space groupP-1(#2) witha= 11.5137(1),b= 16.3208(4),c= 19.1780(5) Å,α= 69.0259(12),β= 84.4955(8),γ= 89.8319(6)°,V= 3347.57(3) Å 3 , andZ= 4 at 295 K. Hydrogen bonds are prominent in the crystal structure. The water molecule forms two medium-strength O–H⋯O hydrogen bonds to one of the esylate anions. The protonated nitrogen atom in each cation forms a N–H⋯O hydrogen bond to an esylate anion. The ring N–H groups form strong intramolecular N–H⋯O hydrogen bonds to carbonyl groups. The ring N–H groups form intramolecular N–H⋯O hydrogen bonds to esylate anions. Many C–H⋅⋅⋅O hydrogen bonds (and one C–H⋯N hydrogen bond), with aromatic C–H, methylene groups and methyl groups as donors, are present. The hydrogen bonding patterns of the two cations differ considerably. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®)

Materials Science↗