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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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DOE OSTI2022

The Preservation of PPARγ Genome Duplicates in Some Teleost Lineages: Insights into Lipid Metabolism and Xenobiotic Exploitation

Three peroxisome proliferator-activated receptor paralogues (PPARα, -β and -γ) are currently recognized in vertebrate genomes. PPARγ is known to modulate nutrition, adipogenesis and immunity in vertebrates. Natural ligands of PPARγ have been proposed; however, the receptor also binds synthetic ligands such as endocrine disruptors. Two paralogues of PPARα and PPARβ have been documented in teleost species, a consequence of the 3R WGD. Recently, two PPARγ paralogue genes were also identified in Astyanax mexicanus. We aimed to determine whether the presence of two PPARγ paralogues is prevalent in other teleost genomes, through genomic and phylogenetic analysis. Our results showed that besides Characiformes, two PPARγ paralogous genes were also identified in other teleost taxa, coinciding with the teleost-specific, whole-genome duplication and with the retention of both genes prior to the separation of the Clupeocephala. To functionally characterize these genes, we used the European sardine (Sardina pilchardus) as a model. PPARγA and PPARγB display a different tissue distribution, despite the similarity of their functional profiles: they are unresponsive to tested fatty acids and other human PPARγ ligands yet yield a transcriptional response in the presence of tributyltin (TBT). This observation puts forward the relevance of comparative analysis to decipher alternative binding architectures and broadens the disruptive potential of man-made chemicals for aquatic species.

Páscoa, Inês↗

DOE OSTI2024

The RNA-binding protein Modulo promotes neural stem cell maintenance in Drosophila

A small population of stem cells in the developing Drosophila central nervous system generates the large number of different cell types that make up the adult brain. To achieve this, these neural stem cells (neuroblasts, NBs) divide asymmetrically to produce non-identical daughter cells. The balance between stem cell self-renewal and neural differentiation is regulated by various cellular machinery, including transcription factors, chromatin remodelers, and RNA-binding proteins. The list of these components remains incomplete, and the mechanisms regulating their function are not fully understood, however. Here, we identify a role for the RNA-binding protein Modulo (Mod; nucleolin in humans) in NB maintenance. We employ transcriptomic analyses to identify RNA targets of Mod and assess changes in global gene expression following its knockdown, results of which suggest a link with notable proneural genes and those essential for neurogenesis. Mod is expressed in larval brains and its loss leads to a significant decrease in the number of central brain NBs. Stem cells that remain lack expression of key NB identity factors and exhibit cell proliferation defects. Mechanistically, our analysis suggests these deficiencies arise at least in part from altered cell cycle progression, with a proportion of NBs arresting prior to mitosis. Overall, our data show that Mod function is essential for neural stem cell maintenance during neurogenesis.

Parra, Amalia S.↗

DOE OSTI2026

Gaussian processes for inferring parton distributions

The extraction of parton distribution functions (PDFs) from experimental or lattice QCD data is an ill-posed inverse problem, where regularization strongly impacts both systematic uncertainties and the reliability of the results. We study a framework based on Gaussian Process Regression (GPR) to reconstruct PDFs from lattice QCD matrix elements. Within a Bayesian framework, Gaussian processes serve as flexible priors that encode uncertainties, correlations, and constraints without imposing rigid functional forms. We investigate a wide range of kernel choices, mean functions, and hyperparameter treatments. We quantify information gained from the data using the Kullback-Leibler divergence. Synthetic data tests demonstrate the consistency and robustness of the method. Our study establishes GPR as a systematic and non-parametric approach to PDF reconstruction, offering controlled uncertainty estimates and reduced model bias in lattice QCD analyses.

hadronic spectroscopy↗

DOE OSTI2022

Ensemble-function relationships to dissect mechanisms of enzyme catalysis

Decades of structure-function studies have established our current extensive understanding of enzymes. However, traditional structural models are snapshots of broader conformational ensembles of interchanging states. We demonstrate the need for conformational ensembles to understand function, using the enzyme ketosteroid isomerase (KSI) as an example. Comparison of prior KSI cryogenic x-ray structures suggested deleterious mutational effects from a misaligned oxyanion hole catalytic residue. However, ensemble information from room-temperature x-ray crystallography, combined with functional studies, excluded this model. Ensemble-function analyses can deconvolute effects from altering the probability of occupying a state (P-effects) and changing the reactivity of each state (k-effects); our ensemble-function analyses revealed functional effects arising from weakened oxyanion hole hydrogen bonding and substrate repositioning within the active site. Ensemble-function studies will have an integral role in understanding enzymes and in meeting the future goals of a predictive understanding of enzyme catalysis and engineering new enzymes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Data‐Driven Insights into Rare Earth Mineralization: Machine Learning Applications Using Functional Material Synthesis Data

Understanding rare‐earth element (REE) mineralization mechanisms is essential for developing efficient separation strategies. Although the geochemical pathways that generate REE deposits are qualitatively known, quantitative links between specific conditions and mineralization outcomes remain limited. Herein, the repurpose laboratory REE hydrothermal synthesis data—originally collected for functional‐materials fabrication—as a surrogate for studying mineralization with data‐driven methods. The compiled 1,200+ hydrothermal reaction records and trained three machine‐learning models—K‐nearest neighbors (KNN), random forest (RF), and extreme gradient boosting (XGB)—to predict product elements and phases from precursors, additives, reaction conditions, and engineered features. Validation shows XGB achieves the highest accuracy. Feature importance indicates thermodynamic properties of cations and anions dominate model decisions. Correlations reveal positive relationships among precursor concentration, reaction time, pH, and temperature, consistent with classical crystallization behavior. XGB‐based regressors are built to predict crystallization temperature and pH from precursor/product attributes. Performance is strongest when similar training examples exist, while accuracy declines for underrepresented reactions, notably REE carbonates and heavy‐REE systems. Overall, the study shows that functional‐materials datasets can illuminate REE mineralization and provide priors for exploration and processing. Expanding datasets with less‐studied chemistries and conditions will improve generality and support deposit discovery and more efficient REE recovery.

feature importance analysis↗

DOE OSTI2021

Improving the characterization of fusion in a MuSIC detector by spatial localization

Multi-Sampling Ionization Chambers (MuSIC) provide an efficient means of measuring nuclear reactions with low beam rates (< 10 6 pps). However, in comparison to thin-target measurements, prior measurements using MuSIC detectors all manifest fusion excitation functions with wide error bars in the energy dimension. This uncertainty limits the applicability of these devices in measuring near and sub-barrier fusion cross-sections. Key to overcoming this limitation is spatial localization of the fusion in the detector. By comparing the measured ionization in the MuSIC detector with accurate energy loss calculations the position of the fusion in the detector is determined. The analysis not only provides the desired improvement in energy resolution, but it also allows extraction of the atomic number of the evaporation residues following fusion. Furthermore, the effectiveness of this approach is demonstrated for 18 O+ 12 C measured with MuSIC@Indiana.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?

Physics-informed neural networks (PINNs) have become a popular choice for solving high-dimensional partial differential equations (PDEs) due to their excellent approximation power and generalization ability. Recently, extended PINNs (XPINNs) based on domain decomposition methods have attracted considerable attention due to their effectiveness in modeling multiscale and multiphysics problems and their parallelization. However, theoretical understanding of their convergence and generalization properties remains unexplored. In this study, we take an initial step towards understanding how and when XPINNs outperform PINNs. Specifically, for general multilayer PINNs and XPINNs, we first provide a prior generalization bound via the complexity of the target functions in the PDE problem and a posterior generalization bound via the posterior matrix norms of the networks after optimization. Moreover, based on our bounds, we analyze the conditions under which XPINNs improve generalization. Concretely, our theory shows that the key building block of XPINN, namely, the domain decomposition, introduces a tradeoff for generalization. On the one hand, XPINNs decompose the complex PDE solution into several simple parts, which decreases the complexity needed to learn each part and boosts generalization. On the other hand, decomposition leads to less training data being available in each subdomain, and hence such a model is typically prone to overfitting and may become less generalizable. Empirically, we choose five PDEs to show when XPINNs perform better than, similar to, or worse than PINNs, hence demonstrating and justifying our new theory.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

The Observed Evolution of the Stellar Mass–Halo Mass Relation for Brightest Central Galaxies

We quantify evolution in the cluster-scale stellar mass–halo mass (SMHM) relation's parameters using 2323 clusters and brightest central galaxies (BCGs) over the redshift range 0.03 ≤ z ≤ 0.60. The precision on the inferred SMHM parameters is improved by including the magnitude gap (m gap ) between the BCG and fourth-brightest cluster member (M14) as a third parameter in the SMHM relation. At fixed halo mass, accounting for m gap , through a stretch parameter, reduces the SMHM relation's intrinsic scatter. To explore this redshift range, we use clusters, BCGs, and cluster members identified using the Sloan Digital Sky Survey C4 and redMaPPer cluster catalogs and the Dark Energy Survey redMaPPer catalog. Through this joint analysis, we detect no systematic differences in BCG stellar mass, m gap , and cluster mass (inferred from richness) between the data sets. We utilize the Pareto function to quantify each parameter's evolution. We confirm prior findings of negative evolution in the SMHM relation's slope (3.5σ), and detect negative evolution in the stretch parameter (4.0σ) and positive evolution in the offset parameter (5.8σ). This observed evolution, combined with the absence of BCG growth, when stellar mass is measured within 50 kpc, suggests that this evolution results from changes in the cluster's m gap . For this to occur, late-term growth must be in the intracluster light surrounding the BCG. We also compare the observed results to IllustrisTNG 300-1 cosmological hydrodynamic simulations and find modest qualitative agreement. However, the simulations lack the evolutionary features detected in the real data.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Scalable Bayesian optimization with randomized prior networks

Several fundamental problems in science and engineering consist of global optimization tasks involving unknown high-dimensional (black-box) functions that map a set of controllable variables to the outcomes of an expensive experiment. Bayesian Optimization (BO) techniques are known to be effective in tackling global optimization problems using a relatively small number objective function evaluations, but their performance suffers when dealing with high-dimensional outputs. To overcome the major challenge of dimensionality, here we propose a deep learning framework for BO and sequential decision making based on bootstrapped ensembles of neural architectures with randomized priors. Using appropriate architecture choices, we show that the proposed framework can approximate functional relationships between design variables and quantities of interest, even in cases where the latter take values in high-dimensional vector spaces or even infinite-dimensional function spaces. In the context of BO, we augmented the proposed probabilistic surrogates with re-parameterized Monte Carlo approximations of multiple-point (parallel) acquisition functions, as well as methodological extensions for accommodating black-box constraints and multi-fidelity information sources. We test the proposed framework against state-of-the-art methods for BO and demonstrate superior performance across several challenging tasks with high-dimensional outputs, including a constrained multi-fidelity optimization task involving shape optimization of rotor blades in turbo-machinery.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Dynamical dark energy in light of the DESI DR2 baryonic acoustic oscillations measurements

Understanding whether cosmic acceleration arises from a cosmological constant or a dynamical component is a central goal of cosmology, and the Dark Energy Spectroscopic Instrument (DESI) enables stringent tests with high-precision distance measurements. Here we analyse measurements of baryon acoustic oscillations in DESI Data Release 1 and Data Release 2 and consider type Ia supernovae and a distance prior for the cosmic microwave background. With the larger statistical power and wider redshift coverage of Data Release 2, the preference for dynamical dark energy does not diminish relative to Data Release 1. Using both a shape-function reconstruction and non-parametric approaches with a Horndeski-motivated correlation prior, we find that the equation of state for dark energy w(z) varies with redshift. Baryon acoustic oscillation data alone yield modest constraints, but in combination with independent supernova compilations and the prior for the cosmic microwave background, they strengthen the evidence for dynamics. A Bayesian comparison of models shows moderate support for departures from Λ cold dark matter (ΛCDM) when several degrees of freedom in w(z) are allowed, corresponding to ~3σ tension with ΛCDM (and higher for some datasets). Despite methodological differences, our results are consistent with companion DESI papers, underscoring the complementarity of the approaches. Possible systematics remain under study; forthcoming DESI, Euclid and next-generation cosmic microwave background data will provide decisive tests.

cosmology↗

DOE OSTI2025

Dynamical dark energy in light of the DESI DR2 baryonic acoustic oscillations measurements

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Recent progress in machine learning methods and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs) have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. Here, the discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods and can be particularly impactful for destructive or irreversible measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Learning Topological Operations on Meshes with Application to Block Decomposition of Polygons

We present a learning based framework for mesh quality improvement on unstructured triangular and quadrilateral meshes. Our model learns to improve mesh quality according to a prescribed objective function purely via self-play reinforcement learning with no prior heuristics. The actions performed on the mesh are standard local and global element operations. The goal is to minimize the deviation of the node degrees from their ideal values, which in the case of interior vertices leads to a minimization of irregular nodes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

In-Field Testing of Components for Feedback and Control of the ITER Disruption Mitigation System

Here, the shattered pellet injection (SPI) method has been chosen as the disruption mitigation system (DMS) for ITER. To protect the device from plasma disruptions that cause damaging heat and electromagnetic loads, SPI is used to inject high-Z material into the plasma. The process of SPI utilizes cryogenic cooling to form solid pellets. Pellets are accelerated down a barrel and into an angled surface, causing the pellet to shatter prior to entering the tokamak chamber. For the DMS to function reliably, the 27 separate shattered pellet injectors planned for ITER must rely on many components to provide accurate feedback data and for control functions. Each component in the DMS is exposed to an elevated background magnetic field depending on its placement and proximity to the plasma chamber. A Helmholtz coil test stand that is operated at Oak Ridge National Laboratory was utilized to test the components in relevant background field levels to assess component performance. This paper details the test design and results for in-field component operation for a variety of components. This list includes the following components: two different network switches for camera connectivity, a VAT fast shutter valve intended to reduce the flow of SPI propellant gas into the torus, a solenoid control valve intended for use in the pellet formation process, pressure/vacuum switches to be used for feedback and control, a printed circuit board piezo pressure sensor to be used to measure breech pressure, and various relays for the high-voltage pulsed power supply used to drive the SPI propellant valve.

Disruption mitigation↗

DOE OSTI2024

Learning energy-based representations of quantum many-body states

Efficient representation of quantum many-body states on classical computers is a problem of practical importance. An ideal representation of a quantum state combines a succinct characterization informed by the structure and symmetries of the system along with the ability to predict the physical observables of interest. Several machine-learning approaches have been recently used to construct such classical representations, which enable predictions of observables and account for physical symmetries. However, the structure of a quantum state typically gets lost unless a specialized is employed based on prior knowledge of the system. Moreover, most such approaches give no information about what states are easier to learn in comparison with others. Here, we propose a generative energy-based representation of quantum many-body states derived from Gibbs distributions used for modeling the thermal states of classical spin systems. Based on the prior information on a family of quantum states, the energy function can be specified by a small number of parameters using an explicit low-degree polynomial or a generic parametric family such as neural nets and can naturally include the known symmetries of the system. Our results show that such a representation can be efficiently learned from data using exact algorithms in a form that enables the prediction of expectation values of physical observables. Importantly, the structure of the learned energy function provides a natural explanation for the difficulty of learning an energy-based representation of a given class of quantum states when measured in a certain basis. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

System Identification of a DC-DC Buck Converter Based on Two-Channel Relay Method

This work presents a system identification approach based on a two-channel relay applied to a DC-DC buck converter operating in closed-loop with a PI controller to regulate its output voltage. The implemented two-relay method allows system identification while the system is running online. Moreover, the algorithm works in a closed-loop configuration while introducing a minimal perturbation in the converter's operation. The presented algorithm was used to identify the frequency response of the converter with no-prior knowledge about the system structure. Furthermore, the identified transfer function parameters has a good agreement with the mathematical model of the converter. Simulation results of the comparison between the switching model, mathematical model and the identified model are provided to validate the theoretical analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Synergistic Density Functional Theory and Molecular Dynamics Approach to Elucidate PNIPAM–Water Interaction Mechanisms

This study employs Density Functional Theory (DFT) and Molecular Dynamics (MD) simulations to investigate interactions between water molecules and Poly(N-isopropylacrylamide) (PNIPAM). DFT reveals preferential water binding sites, with enhanced binding energy observed in the linker zone. Quantum Theory of Atoms in Molecules (QTAIM) and electron localization function (ELF) analyses highlight the roles of hydrogen bonding and steric hindrance. MD simulations unveil temperature-dependent hydration dynamics, with structural transitions marked by changes in the radius of gyration (Rg) and the radial distribution function (RDF), aligning with DFT findings. Our work goes beyond prior studies by combining a DFT, QTAIM and MD simulations approach across different PNIPAM monomer-to-30mer structures. It introduces a systematic quantification of pseudo-saturation thresholds and explores water clustering dynamics with structural specificity, which have not been previously reported in the literature. These novel insights establish a more complete molecular-level picture of PNIPAM hydration behavior and temperature responsiveness, emphasizing the importance of amide hydrogen and carbonyl oxygen sites in hydrogen bonding, which weakens above the lower critical solution temperature (LCST), resulting in increased hydrophobicity and paving the way for understanding water sorption mechanisms, offering guidance for future applications such as dehumidification and atmospheric water harvesting.

08 HYDROGEN↗

DOE OSTI2023

Progress Towards the Reality of Low-Temperature Critical Experiments with Low-Temperature TEX

Integral benchmarks for criticality safety do not exist below room temperature. Incredible support for below room temperature benchmarks is present across the international community. Validation of low-temperature neutron cross sections is necessary to verify any operation at temperatures below room temperature which is typically observed in environments far from the equator. For example, a fissile material transportation truck may routinely observe ambient temperatures down to -40°C, which is the lower temperature bound of the normal conditions of transportation defined in the United States Title 10 Code of Federal Regulations §71.71c2. Similarly, the IAEA, which contains 176 member states, has the same requirement for subcriticality for fissile material transportation. Additionally, sub-room temperature benchmarks can validate newly produced cross sections, that include novel thermal scattering laws, from North Carolina State University. A six-case experiment designed based off the TEX-HEU baseline experiments has been proposed utilizing a chilled vacuum chamber capable of cooling the HEU stack down to -50°C. The six proposed configurations span the entire neutron fission energy range and can be tested at intermediate temperatures between room temperature and -40°C. Of the six configurations, the fastest case utilizes nearly 135 kg of HEU. Understanding the system is required prior to critical experiment operations. In order to test the functionality of the system, testing with surrogate fuel materials has been conducted. The following discusses the design of the low temperature TEX (LT-TEX) experiment as well as the progress made via surrogate testing towards a reality in which low-temperature integral benchmarks exist in the International Criticality Safety Benchmark Evaluation Project (ICSBEP).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗