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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 811 records · Page 45

Investigating the Role of Defects in α-Pu Phase Transitions using Density Functional Theory

Before reaching its relatively low melting point of 913 K, plutonium undergoes six solid state phases: simple monoclinic alpha, body-centered monoclinic beta, face-centered orthorhombic gamma, face centered cubic delta, body-centered tetragonal delta-prime, and body-centered cubic epsilon phases. Due to their low symmetry, the monoclinic alpha and beta phases have not been studied extensively, however it has been previously hypothesized that the transition between alpha and beta is caused by defects in the lattices due to the incommensurate number of atoms. Additionally, the martensitic delta to alpha phase transformation in plutonium-gallium alloys induces an expanded alpha lattice known as alpha prime, most likely due to the insoluble gallium impurity atom. This work explores how defects such as plutonium interstitials and gallium interstitials and substitutionals may affect the alpha plutonium lattice, providing a better understanding of the mechanisms for the alpha-beta and martensitic phase transformations through density functional theory (DFT). Results indicate that plutonium interstitials within the alpha lattice induce beta features structurally and electronically, such as volume expansion and bonding changes, and gallium interstitials and substitutionals within the alpha lattice induce volume changes as observed experimentally in alpha prime.

36 MATERIALS SCIENCE↗

Quantum surrogate models for uncertainty quantification

Surrogate models are a critical ingredient to computation-based design and validation of many DOE mission-relevant physical systems. When first-principles computation of properties of a physical systems becomes pro hibitive, surrogate models are the only path towards achieving tasks such as uncertainty quantification (UQ), exploration of design space, and validation of design choices. In this project we have developed and demonstrated a new surro gate modeling paradigm for complex models that is data-driven, non-intrusive, and has the potential to be versatile and equipped with performance guaran tees. This combination of features is absent in existing surrogate modeling tools. The framework we have developed in this project exploits a quantum-classical correspondence to establish a quantum system that mimics the dynamics of the classical Hamiltonian system from which data in the form of temporal snapshots is provided. Since quantum dynamics propagates distributions over observables, the framework is naturally suited to propagation of epistemic uncertainties in the form of distributions over initial state and parametric uncertainties. In this project, we take the first step in establishing this novel framework by deriving a quantization and de-quantization procedure, demonstrating the accuracy of the quantum surrogate models these define using two model systems, and defining the next steps in maturing the framework towards a tool applicable to Sandia mission-relevant problems.

97 MATHEMATICS AND COMPUTING↗

Half-ice, half-fire-driven ultranarrow phase crossover in one-dimensional decorated 𝑞-state Potts ferrimagnets: An AI-co-led exploration

OpenAI’s reasoning model o3-mini-high was used to carry out an exact analytic study of one-dimensional ferrimagnetic site- and bond-decorated 𝑞-state Potts models. We demonstrate that the finite-temperature ultranarrow phase crossover (UNPC), driven by a hidden “half-ice, half-fire” state recently discovered in the 𝑞=2 case (Ising model), persists for 𝑞>2. Moreover, we identify unique features for 𝑞>2, including the dome structure in the field-temperature phase diagram, and for large 𝑞 a secondary high-temperature UNPC to the fully disordered paramagnetic state. As the UNPC quickly approaches a genuine transition by enhancing 𝐽, the interaction between the backbone spins, two distinct behaviors emerge: In the site-decorated Potts model, 𝑇 0 is independent of 𝐽 and thus remains unchanged (Type-I UNPC), and in the bond-decorated Potts model with 𝑞>2, 𝑇 0 depends on 𝐽 and quickly shifts toward a finite temperature as 𝐽 increases (Type-II UNPC). These results establish a versatile framework for engineering controlled fast state-flipping switches in low-dimensional systems. Our nine-dan artificial intelligence (AI)-contribution framework assigns AI the meritorious status of AI-co-led discovery in this work.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Direct Prediction of Phonon Density of States With Euclidean Neural Networks

Abstract Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges remain for continuous property prediction. The challenge is aggravated in crystalline solids due to crystallographic symmetry considerations and data scarcity. Here, the direct prediction of phonon density‐of‐states (DOS) is demonstrated using only atomic species and positions as input. Euclidean neural networks are applied, which by construction are equivariant to 3D rotations, translations, and inversion and thereby capture full crystal symmetry, and achieve high‐quality prediction using a small training set of examples with over 64 atom types. The predictive model reproduces key features of experimental data and even generalizes to materials with unseen elements, and is naturally suited to efficiently predict alloy systems without additional computational cost. The potential of the network is demonstrated by predicting a broad number of high phononic specific heat capacity materials. The work indicates an efficient approach to explore materials' phonon structure, and can further enable rapid screening for high‐performance thermal storage materials and phonon‐mediated superconductors.

97 MATHEMATICS AND COMPUTING↗

Fate and transport of unruptured tri-structural isotropic (TRISO) fuel particles in the event of environmental release for advanced and micro reactor applications

Advanced nuclear reactor designs and advanced fuel types offer safety features that may reduce environmental consequences in an accident scenario when compared to conventional reactors and fuels. One advanced reactor fuel is tri-structural isotropic (TRISO) fuel particles which are approximately 0.9 mm in diameter. TRISO particle mobility, assuming the particle is unruptured and the encapsulated radionuclides are contained, was explored through a theoretical examination of mobility through atmospheric, soil and groundwater, surface water, and non-human biota transport pathways. TRISO particles are too large and dense to travel in the atmosphere except under extreme conditions. TRISO particles are too large to penetrate most soil profiles and so cannot be transported with groundwater. TRISO particles will settle out of the water column in surface waters but the transport will depend on the energy of the body of water and likelihood of extreme dispersion events. TRISO particles could be transported by non-human biota. The size of TRISO particles could allow them to be intentionally moved by non-human biota if they are ingested as a gastrolith or if they are mistaken for something in the organism’s diet. Generally, TRISO particles will have reduced environmental mobility compared to releases of radionuclides in the event of an accident in a conventional nuclear reactor. The extent of transport has implications in emergency planning zone designations and other considerations for licensing and deploying TRISO-fueled reactors. Finally, further research and experimental work exploring TRISO particle mobility is required to understand the full environmental mobility of TRISO particles.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Enhancing cold spray coatings: Microstructural dynamics and performance attributes of Inconel 625 with chromium carbide incorporation for hydropower applications

The incorporation of chromium carbide (CrC) particles into the cold spray (CS) process is known to mitigate nozzle clogging, although at the expense of deposition efficiency. This study explores the intricate microstructural changes induced by varying amounts of CrC powder (12.5 % and 6 %) in conjunction with Inconel-625 (Inc-625) powder. The deposition was carried out onto A27 cast steel under different CS parameters. Microstructural characterization, including detailed electron microscopy studies, reveals a complex yet structurally stable coating. Noteworthy features include grain fragmentation and a cellular structure enriched with Nb and Mo, with minimal plastic deformation of CrC in the matrix. The cold-sprayed coatings exhibit a significant (~4 times) increase in microhardness compared to the A27 substrate. Mechanical and cavitation erosion properties were systematically investigated. Coatings subjected to higher particle energy conditions with a gas pressure of 600 psi and gas temperature of 650 °C, demonstrated superior resistance to cavitation erosion. This resistance is attributed to a combination of factors, including microstructural characteristics and porosity. Altogether, the study provides valuable insights into the structural dynamics and performance of CS coatings enriched with CrC particles.

A27 cast steel↗

Spin–Flop and Metamagnetic Transition in Monoclinic Eu 4 Bi 6 Se 13

This study explores an investigation of the crystallographic, electronic, and magnetic properties of the europium-based bismuth selenide compound Eu 4 Bi 6 Se 13 , with particular focus on its magnetic anisotropy. This compound adopts a monoclinic crystal structure classified under the P2 1 /m space group (#11). It exhibits distinctive structural features, including substantial Eu–Se coordination numbers (6 and 8), Bi–Se ladders, and linear chains of Eu atoms that propagate along the b-axis. Electronic resistivity assessments indicate that Eu 4 Bi 6 Se 13 exhibits metallic behavior. As the magnetic field is oriented along the b-axis, magnetic characterization reveals uniaxial magnetic anisotropy, with metamagnetic transitions appearing at approximately 12 kOe and a lower field. In the field below 10 kOe, the spin-flop transition is observed with possible domain-induced hysteresis. This behavior supports the identification of metamagnetic features in field-dependent measurements attributable to the europium spins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Stability of Metal–Organic Framework-Supported Amines under Exposure to Ozone Generated from Air

Amine compounds supported on porous materials such as metal–organic frameworks (MOFs) have shown promising performance for direct air capture (DAC) due to their enhanced affinity for CO 2 . Although features such as adsorption capacity and selectivity are paramount in these composites, their long-term stability has a major impact on the operating cost of DAC systems. In this work, changes in carbon capture performance, crystallinity, porosity and chemical environment of the constituting atoms of MOF-amine composites are explored after exposure to ozone and NOx impurities generated from corona discharge applied to air. From the obtained results, the stabilities of Mg 2 (dobpdc) (dobpdc 4– = 4,4′-dioxidobiphenyl-3,3′-dicarboxylate) grafted with ethylenediamine (en), N-methylethylenediamine (men), and N,N-dimethylethylenediamine (dmen), as well as MIL-101(Cr) MOF impregnated with polyethylenimine (PEI), are compared. A negative effect in the overall CO 2 adsorption capacity is observed for all MOF composites after exposure, as well as a decrease in the adsorption step pressure of CO 2 for Mg 2 (dobpdc) amine-grafted composites, as shown via dynamic gravimetric adsorption experiments. Spectroscopic analyses indicate that oxidation of amine groups through the formation of nitro functional groups occurs as well as a decrease in the electron-donation interaction between the supported amines and the metal nodes of the MOFs.

adsorption↗

Applied Machine-Learning Models to Identify Spectral Sub-Types of M Dwarfs from Photometric Surveys

M dwarfs are the most abundant stars in the Solar Neighborhood and they are prime targets for searching for rocky planets in habitable zones. Consequently, a detailed characterization of these stars is in demand. The spectral sub-type is one of the parameters that is used for the characterization and it is traditionally derived from the observed spectra. However, obtaining the spectra of M dwarfs is expensive in terms of observation time and resources due to their intrinsic faintness. We study the performance of four machine-learning (ML) models—K-Nearest Neighbor (KNN), Random Forest (RF), Probabilistic Random Forest (PRF), and Multilayer Perceptron (MLP)—in identifying the spectral sub-types of M dwarfs at a grand scale by deploying broadband photometry in the optical and near-infrared. We trained the ML models by using the spectroscopically identified M dwarfs from the Sloan Digital Sky Survey (SDSS) Data Release (DR) 7, together with their photometric colors that were derived from the SDSS, Two-Micron All-Sky Survey, and Wide-field Infrared Survey Explorer. We found that the RF, PRF, and MLP give a comparable prediction accuracy, 74%, while the KNN provides slightly lower accuracy, 71%. We also found that these models can predict the spectral sub-type of M dwarfs with ~99% accuracy within ±1 sub-type. The five most useful features for the prediction are r - z, r - i, r - J, r - H , and g - z, and hence lacking data in all SDSS bands substantially reduces the prediction accuracy. However, we can achieve an accuracy of over 70% when the r and i magnitudes are available. Since the stars in this study are nearby (d ≲ 1300 pc for 95% of the stars), the dust extinction can reduce the prediction accuracy by only 3%. Finally, we used our optimized RF models to predict the spectral sub-types of M dwarfs from the Catalog of Cool Dwarf Targets for the Transiting Exoplanet Survey Satellite, and we provide the optimized RF models for public use.

79 ASTRONOMY AND ASTROPHYSICS↗

HostSub_GP: Precise Galaxy Background Subtraction in Transient Long-slit Spectroscopy with Gaussian Processes

We present a novel host galaxy subtraction technique in long-slit spectroscopy for extragalactic transients. Unlike classic methods which generally estimate the background using simple interpolation of local galaxy flux in the 2D spectrum, our approach leverages multi-band archival images of the host galaxies to model the background emission from the galaxy in the 2D spectrum. Such imaging encodes the wavelength-dependent galaxy profile along the slit, and is readily accessible through wide-field imaging surveys. We construct a smooth prior for the 2D galaxy profile with a Gaussian process (GP) based on these reference images, and use another GP to model the correlated deviations from the prior in the observed spectrum. This enables accurate inference of the galaxy flux blended with the transient. On synthetic long-slit data of a spiral galaxy extracted from a Multi Unit Spectroscopic Explorer hyper-spectral cube, the GP method remains robust as long as the host galaxy is spatially resolved and consistently outperforms classic methods. We apply the method to archival Keck spectra of two real transients, SN 2019eix and AT 2019qiz, to further demonstrate how the method uniquely recovers weak spectral features amid strong galaxy contamination, enabling refined constraints on the properties of both transients. We have released the software implementation, HostSub_GP, a scalable toolkit that leverages JAX, with an MIT license.

79 ASTRONOMY AND ASTROPHYSICS↗

A high-throughput experimentation platform for data-driven discovery in electrochemistry

Automating electrochemical analyses combined with artificial intelligence is poised to accelerate discoveries in renewable energy sciences and technologies. This study presents an automated high-throughput electrochemical characterization (AHTech) platform as a cost-effective and versatile tool for rapidly assessing liquid analytes. The Python-controlled platform combines a liquid handling robot, potentiostat, and customizable microelectrode bundles for diverse, reproducible electrochemical measurements in microtiter plates, minimizing chemical consumption and manual effort. To showcase the capability of AHTech, we screened a library of 180 small molecules as electrolyte additives for aqueous zinc metal batteries, generating data for training machine learning models to predict Coulombic efficiencies. Key molecular features governing additive performance were elucidated using Shapley Additive exPlanations and Spearman’s correlation, pinpointing high-performance candidates like cis-4-hydroxy-d-proline, which achieved an average Coulombic efficiency of 99.52% over 200 cycles. The workflow established herein is highly adaptable, offering a powerful framework for accelerating the exploration and optimization of extensive chemical spaces across diverse energy storage and conversion fields.

Lin, Dian-Zhao [Johns Hopkins University, Baltimor↗

ANDES (ARGONNE NUCLEAR DATA EXPLORATION SOFTWARE) - ndviz

The Argonne Nuclear Data Exploration Software (ANDES) is a collection of open-source C++ codes and ROOT scripts that enable the visualization of patterns emerging from nuclear data. From this global view, well-known phenomena such as major nuclear shell closures and pairing effects near N=Z nuclei, as well as more subtle features, such as quantum phase transition around the Zr isotopes or the charge radius staggering in the Hg isotopes, can be observed. This visualization tool apart from having pedagogic application can serve as a tool for discovering previously unknown phenomena in the nuclear chart.

Santiago-Gonzalez, Daniel↗

ANDES (ARGONNE NUCLEAR DATA EXPLORATION SOFTWARE) - Physics Tools

A collection of physics tools used by the ANDES. The Argonne Nuclear Data Exploration Software (ANDES) is a collection of open-source C++ codes and ROOT scripts that enable the visualization of patterns emerging from nuclear data. From this global view, well-known phenomena such as major nuclear shell closures and pairing effects near N=Z nuclei, as well as more subtle features, such as quantum phase transition around the Zr isotopes or the charge radius staggering in the Hg isotopes, can be observed. This visualization tool apart from having pedagogic application can serve as a tool for discovering previously unknown phenomena in the nuclear chart.

Santiago-Gonzalez, Daniel↗

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein–ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure–activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure–activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein–ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

3D structural interaction fingerprints↗

Recent progress in atomic-scale controlled plasma processing

Atomic-scale control in plasma processing is becoming increasingly critical for fabricating of advanced semiconductor devices, particularly as the industry shifts toward three-dimensional (3D) architectures and high-aspect-ratio (HAR) structures. This review presents a comprehensive overview of recent developments in atomic-scale controlled plasma processes, organized along two key directions: the hierarchical structure of plasma–surface interactions and the generational evolution of atomic layer processing (ALP) technologies. We examined the gas phase, where molecular design enables selective generation of ions and radicals; the boundary layer, where transport phenomena govern species delivery into nanoscale features, and the surface, where temperature-dependent reactions and cyclic processing determine etching selectivity and precision. Building on this foundation, we outline five generations of ALP—from thermal atomic layer deposition to transport-aware, temporally and structurally decoupled processes—highlighting the increasing sophistication of process control. The review further explores the transition from empirical recipe development to science-based, data-driven methodologies. By integrating quantum-chemical modeling, advanced diagnostics, and machine learning, we demonstrated how predictive models can link plasma species composition to process outcomes, enabling autonomous and adaptive control strategies. Finally, this review discusses the broader societal implications of plasma process innovation through the E4 quartet: energy and resource efficiency, environmental sustainability, evolutionary advancement, and educational promotion. These principles guide the development of sustainable and intelligent atomic-scale manufacturing technologies that are not only technically advanced but also socially responsible.

Ishikawa, Kenji [Nagoya Univ. (Japan)] (ORCID:0000↗

Multi-century dynamics of the climate and carbon cycle under both high and net negative emissions scenarios

Abstract. Future climate projections from Earth system models (ESMs) typically focus on the timescale of this century. We use a set of five ESMs and one Earth system model of intermediate complexity (EMIC) to explore the dynamics of the Earth's climate and carbon cycles under contrasting emissions trajectories beyond this century to the year 2300. The trajectories include a very-high-emissions, unmitigated fossil-fuel-driven scenario, as well as a mitigation scenario that diverges from the first scenario after 2040 and features an “overshoot”, followed by a decrease in atmospheric CO2 concentrations by means of large net negative CO2 emissions. In both scenarios and for all models considered here, the terrestrial system switches from being a net sink to either a neutral state or a net source of carbon, though for different reasons and centered in different geographic regions, depending on both the model and the scenario. The ocean carbon system remains a sink, albeit weakened by carbon cycle feedbacks, in all models under the high-emissions scenario and switches from sink to source in the overshoot scenario. The global mean temperature anomaly is generally proportional to cumulative carbon emissions, with a deviation from proportionality in the overshoot scenario that is governed by the zero emissions commitment. Additionally, 23rd century warming continues after the cessation of carbon emissions in several models in the high-emissions scenario and in one model in the overshoot scenario. While ocean carbon cycle responses qualitatively agree in both globally integrated and zonal mean dynamics in both scenarios, the land models qualitatively disagree in zonal mean dynamics, in the relative roles of vegetation and soil in driving C fluxes, in the response of the sink to CO2, and in the timing of the sink–source transition, particularly in the high-emissions scenario. The lack of agreement among land models on the mechanisms and geographic patterns of carbon cycle feedbacks, alongside the potential for lagged physical climate dynamics to cause warming long after CO2 concentrations have stabilized, points to the possibility of surprises in the climate system beyond the 21st century time horizon, even under relatively mitigated global warming scenarios, which should be taken into consideration when setting global climate policy.

54 ENVIRONMENTAL SCIENCES↗

Uncertainty-informed selection of CMIP6 Earth System Model subsets for use in multisectoral and impact models

Earth system models (ESMs) and general circulation models (GCMs) are heavily used to provide inputs to sectoral impact and multisector dynamic models, which include representations of energy, water, land, economics, and their interactions. Therefore, representing the full range of model uncertainty, scenario uncertainty, and interannual variability that ensembles of these models capture is critical to the exploration of the future co-evolution of the integrated human–Earth system. The pre-eminent source of these ensembles has been the Coupled Model Intercomparison Project (CMIP). With more modeling centers participating in each new CMIP phase, the size of the model archive is rapidly increasing, which can be intractable for impact modelers to effectively utilize due to computational constraints and the challenges of analyzing large datasets. In this work, we present a method to select a subset of the latest phase, CMIP6, featuring models for use as inputs to a sectoral impact or multisector dynamics models, while prioritizing preservation of the range of model uncertainty, scenario uncertainty, and interannual variability in the full CMIP6 ensemble results. This method is intended to help impact modelers select climate information from the CMIP archive efficiently for use in downstream models that require global coverage of climate information. This is particularly critical for large-ensemble experiments of multisector dynamic models that may be varying additional features beyond climate inputs in a factorial design, thus putting constraints on the number of climate simulations that can be used. We focus on temperature and precipitation outputs of CMIP6 models, as these are two of the most used variables among impact models, and many other key input variables for impacts are at least correlated with one or both of temperature and precipitation (e.g., relative humidity). Besides preserving the multi-model ensemble variance characteristics, we prioritize selecting CMIP6 models in the subset that preserve the very likely distribution of equilibrium climate sensitivity values as assessed by the latest Intergovernmental Panel on Climate Change (IPCC) report. This approach could be applied to other output variables of climate models and, possibly when combined with emulators, offers a flexible framework for designing more efficient experiments on human-relevant climate impacts. It can also provide greater insight into the properties of existing CMIP6 models.

Snyder, Abigail C.↗

Exploring Geothermal Potential of Great Basin Sub-Regions: Preprint

The INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) project aims to discover new, economically viable hidden geothermal systems in the Great Basin region by building on previous work in play fairway analysis and machine learning. A key objective of this project is to develop an exploration workflow to reduce geothermal exploration risks for hidden geothermal systems. A single preliminary play fairway workflow was developed from the assessment of the regional INGENIOUS geological, geophysical, and geochemical datasets. This workflow provided new preliminary predictive geothermal fairway maps for the INGENIOUS study area, which encompasses most of Nevada, western Utah, southern Idaho, southeastern Oregon, and easternmost California. However, a recent study (incorporating machine learning techniques) of a portion of Nevada identified four geologic domains and determined that the relative importance of individual datasets or features as indicators of geothermal potential may differ across these domains. The INGENIOUS study area includes a much larger and more geologically diverse region; therefore, additional geologic domains or sub-regions are expected. To assess the sub-regions in the INGENIOUS study area, principal component analysis and k-means clustering were applied. Preliminary results indicate that the INGENIOUS regional data cluster into groups that relate to different geologic domains in the Great Basin region. These include domains such as the Walker Lane, extensional western Great Basin region, broad lower strain region in the eastern Great Basin of western Utah and eastern Nevada, Quaternary volcanic fields, and the area adjacent to the Snake River Plain. These clusters are assessed to determine the key geologic drivers of the identified clusters. Understanding this variability can provide key insights for the exploration and characterization of hidden geothermal systems in the Great Basin region and could indicate the need to develop multiple geothermal conceptual models and play fairway workflows for the INGENIOUS study area.

GEOTHERMAL ENERGY↗