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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Water Mass Transformation Budgets in Finite‐Volume Generalized Vertical Coordinate Ocean Models

Water Mass Transformation (WMT) theory provides conceptual tools that in principle enable innovative analyses of numerical ocean models; in practice, however, these methods can be challenging to implement and interpret, and therefore remain under-utilized. Our aim is to demonstrate the feasibility of diagnosing all terms in the water mass budget and to exemplify their usefulness for scientific inquiry and model development by quantitatively relating water mass changes, overturning circulations, boundary fluxes, and interior mixing. We begin with a pedagogical derivation of key results of classical WMT theory. We then describe best practices for diagnosing each of the water mass budget terms from the output of Finite-Volume Generalized Vertical Coordinate (FV-GVC) ocean models, including the identification of a non-negligible remainder term as the spurious numerical mixing due to advection scheme discretization errors. We illustrate key aspects of the methodology through the analysis of a polygonal region of the Greater Baltic Sea in a regional demonstration simulation using the Modular Ocean Model v6 (MOM6). We verify the convergence of our WMT diagnostics by brute-force, comparing time-averaged (“offline”) diagnostics on various vertical grids to timestep-averaged (“online”) diagnostics on the native model grid. Finally, we briefly describe a stack of xarray-enabled Python packages for evaluating WMT budgets in FV-GVC models (culminating in the new xwmb package), which is intended to be model-agnostic and available for community use and development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Novel Deep Learning Transformer Model for Short to Sub‐Seasonal Streamflow Forecast

Accurate short-to-subseasonal streamflow forecasts are becoming crucial for effective water management in an increasingly variable climate. However, streamflow forecast remains challenging over extended lead times, uncertainty in meteorological inputs, and increased frequency and variability in extreme weather and climate events. We implemented a Future Time Series Transformer (FutureTST) model for streamflow forecasting that separately integrates past meteorological and streamflow data while incorporating future weather conditions. FutureTST achieves a mean Nash-Sutcliffe Efficiency (NSE) of 0.82 to 0.67 for 1- to 30-day streamflow forecasts. Incorporating upstream streamflow information improved forecast accuracy by up to 10%. During real-time forecast, FutureTST maintains higher forecast skills of 9.03 for 1-day and 5.74 for 14-day forecasts. In contrast, calibrated process-based hydrological model forecasts become unreliable beyond a 4-day lead time. Our findings demonstrate the potential of FutureTST as a reliable streamflow forecasting tool that offers a valuable addition to operational flood monitoring systems and climate-resilient decision-making.

Ambika, Anukesh Krishnankutty [Oak Ridge National ↗
DOE OSTI2024

De novo atomic protein structure modeling for cryoEM density maps using 3D transformer and HMM

Accurately building 3D atomic structures from cryo-EM density maps is a crucial step in cryo-EM-based protein structure determination. Converting density maps into 3D atomic structures for proteins lacking accurate homologous or predicted structures as templates remains a significant challenge. Here, we introduce Cryo2Struct, a fully automated de novo cryo-EM structure modeling method. Cryo2Struct utilizes a 3D transformer to identify atoms and amino acid types in cryo-EM density maps, followed by an innovative Hidden Markov Model (HMM) to connect predicted atoms and build protein backbone structures. Cryo2Struct produces substantially more accurate and complete protein structural models than the widely used ab initio method Phenix. Additionally, its performance in building atomic structural models is robust against changes in the resolution of density maps and the size of protein structures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Unusual plastic strain-induced phase transformation phenomena in silicon

Pressure-induced phase transformations (PTs) in Si, the most important electronic material, have been broadly studied, whereas strain-induced PTs have never been studied in situ. Here, we reveal in situ various important plastic strain-induced PT phenomena. A correlation between the direct and inverse Hall-Petch effect of particle size on yield strength and pressure for strain-induced PT is predicted theoretically and confirmed experimentally for Si-I→Si-II PT. For 100 nm particles, the strain-induced PT Si-I→Si-II initiates at 0.3 GPa under both compression and shear while it starts at 16.2 GPa under hydrostatic conditions. The Si-I→Si-III PT starts at 0.6 GPa but does not occur under hydrostatic pressure. Pressure in small Si-II and Si-III regions of micron and 100 nm particles is ~5–7 GPa higher than in Si-I. For 100 nm Si, a sequence of Si-I → I + II → I + II + III PT is observed, and the coexistence of four phases, Si-I, II, III, and XI, is found under torsion. Retaining Si-II and single-phase Si-III at ambient pressure and obtaining reverse Si-II→Si-I PT demonstrates the possibilities of manipulating different synthetic paths. The obtained results corroborate the elaborated dislocation pileup-based mechanism and have numerous applications for developing economic defect-induced synthesis of nanostructured materials, surface treatment (polishing, turning, etc.), and friction.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Transforming jet flavour tagging at ATLAS

Jet flavour tagging enables the identification of jets originating from heavy-flavour quarks in proton–proton collisions at the Large Hadron Collider, playing a critical role in its physics programmes. This paper presents GN2, a transformer-based flavour tagging algorithm deployed by the ATLAS Collaboration that represents a different methodology compared to previous approaches. Designed to classify jets based on the flavour of their constituent particles, GN2 processes low-level tracking information in an end-to-end architecture and incorporates physics-informed auxiliary training objectives to enhance both interpretability and performance. Its performance is validated in both simulation and collision data. The measured c-jet (light-jet) rejection in data is improved by a factor of 3.5 (1.8) for a 70% b-jet tagging efficiency, compared to the previous algorithm. GN2 provides substantial benefits for physics analyses involving heavy-flavour jets, such as measurements of Higgs boson pair production and the couplings of bottom and charm quarks to the Higgs boson, and demonstrates the impact of advanced machine learning methods in experimental particle physics.

Characterization and analytical techniques↗
DOE OSTI2024

Quantitative kinetic rules for plastic strain-induced α - ω phase transformation in Zr under high pressure

Plastic strain-induced phase transformations (PTs) and chemical reactions under high pressure are broadly spread in modern technologies, friction and wear, geophysics, and astrogeology. However, because of very heterogeneous fields of plastic strain $E$ p and stress σ tensors and volume fraction c of phases in a sample compressed in a diamond anvil cell (DAC) and impossibility of measurements of σ and $E$ p , there are no strict kinetic equations for them. Here, we develop a kinetic model, finite element method (FEM) approach, and combined FEM-experimental approaches to determine all fields in strongly plastically predeformed Zr compressed in DAC, and specific kinetic equation for α-ω PT consistent with experimental data for the entire sample. Since all fields in the sample are very heterogeneous, data are obtained for numerous complex 7D paths in the space of 3 components of the plastic strain tensor and 4 components of the stress tensor. Kinetic equation depends on accumulated plastic strain (instead of time) and pressure and is independent of plastic strain and deviatoric stress tensors, i.e., it can be applied for various above processes. Our results initiate kinetic studies of strain-induced PTs and provide efforts toward more comprehensive understanding of material behavior in extreme conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Transforming an ATP-dependent enzyme into a dissipative, self-assembling system

Nucleoside triphosphate (NTP)-dependent protein assemblies such as microtubules and actin filaments have inspired the development of diverse chemically fueled molecular machines and active materials but their functional sophistication has yet to be matched by design. Given this challenge, we asked whether it is possible to transform a natural adenosine 5'-triphosphate (ATP)-dependent enzyme into a dissipative self-assembling system, thereby altering the structural and functional mode in which chemical energy is used. Furthermore we report that FtsH (filamentous temperature-sensitive protease H), a hexameric ATPase involved in membrane protein degradation, can be readily engineered to form one-dimensional helical nanotubes. FtsH nanotubes require constant energy input to maintain their integrity and degrade over time with the concomitant hydrolysis of ATP, analogous to natural NTP-dependent cytoskeletal assemblies. Yet, in contrast to natural dissipative systems, ATP hydrolysis is catalyzed by free FtsH protomers and FtsH nanotubes serve to conserve ATP, leading to transient assemblies whose lifetimes can be tuned from days to minutes through the inclusion of external ATPases in solution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Roadmap for transforming heterogeneous catalysis with artificial intelligence

Artificial intelligence (AI) is poised to transform heterogeneous catalysis, opening avenues for catalytic materials discovery. By uncovering intricate patterns in high-dimensional data, AI has been reshaping our pursuit of sustainable catalytic processes across the energy, environmental and chemical sectors. This promise, however, hinges on overcoming fundamental barriers, including limitations in data availability and quality, challenges in the generalizability and interpretability of data-augmented decisions, and the persistent gap between in silico predictions and experiments. Furthermore, we outline a forward-looking roadmap for deeply integrating AI into heterogeneous catalysis with an AI-ready data ecosystem, multimodal foundation models, and ultimately autonomous laboratories to accelerate the development of next-generation catalytic technologies via AI-empowered human–machine collaboration.

Computational methods↗
DOE OSTI2024

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.

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

Probing the proton exchange kinetics of BaZr 0.1 Ce 0.7 Y 0.1 Yb 0.1 O 3− δ ceramic electrolyte by operando diffuse reflectance infrared Fourier transform spectroscopy

Proton exchange kinetics plays an important role in governing the performance of intermediate-temperature protonic ceramic electrolysis cells (PCECs) for hydrogen production. Our understanding of the nature of the surface hydration reaction at the single-cell level, however, remains very limited, hampering further efficiency improvements. Here, in this study, we developed a custom operando diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) platform that operates under high temperature and steam conditions with applied bias. Quantitative investigations of surface H 2 O/D 2 O isotope exchange in a BaZr 0.1 Ce 0.7 Y 0.1 Yb 0.1 O 3−δ (BZCYYb1711) protonic electrolyte-based single cell were conducted under different applied voltages using this DRIFTS platform, to gain molecular-level insight into hydration kinetics. The findings show that the application of an external voltage significantly enhances the surface proton exchange rate, decreasing the apparent activation energy from 29.1 kJ mol −1 at open-circuit voltage (OCV) to 6.8 kJ mol −1 at 1.3 V. In addition, distinct voltage-induced spectral shifts in O–D vibrations point to dynamic changes in surface hydration. These findings demonstrate a sensitive spectroscopic platform for probing interfacial proton processes and reveal strong electrochemical control over surface proton kinetics, offering new opportunities for probing electrolyte hydration behavior in PCECs.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

The ever-evolving active site: transformation of single atoms to extended structures during the Rh-catalyzed reverse water–gas shift reaction

At low temperatures (< 400°C), single atoms of Rh supported on rutile TiO 2 (rTiO 2 ) are responsible for the formation of CO during the reverse water gas shift (RWGS), while methane production is associated with the Rh-TiO 2 interface due to the correlation between methane formation rates and the volume-averaged Rh nanoparticle diameter. As the temperature is increased to >540°C, there is a notable increase in CO selectivity and methane production rates tend towards zero. The time to reach zero depends on the temperature but independent of the initial Rh structure (single atoms and/or nanoparticles), which is controlled by the catalyst preparation method (wetness impregnation versus colloidal nanoparticles). At 600°C and > 4 h time on stream, the catalytic behaviour becomes completely agnostic to the initial Rh structure as well as weight loading, and the catalysts are highly selective for the reverse water gas-shift reaction. Post-reaction HR-TEM image analysis confirms Rh nanoparticles crystallize/order during the reaction; at 400°C, most of the Rh particles are disordered, while at 600°C, they are more ordered (i.e., development of defined faceting). Infrared spectroscopy of CO adsorption on Rh nanoparticles confirms the appearance of defined facets after annealing in nitrogen at high temperatures. Annealing the Rh/rTiO 2 catalysts prior to the RWGS reaction demonstrates the structural transformation of Rh depends only on time and temperature and not on reactant or product fugacity. Sites responsible for stabilizing Rh single atoms are no longer competent at higher temperatures, enabling their integration into existent nanoparticles. As the reaction temperature is increased to temperatures >540°C, the dominant active site for CO production evolves from single atoms to extended Rh structures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Single-atom molybdenum doping induces nickel oxide-to-hydroxide transformation for enhanced alkaline hydrogen evolution

NiMoO x compounds are widely regarded as among the most efficient non-noble metal catalysts for the hydrogen evolution reaction (HER). Nevertheless, understanding the structural evolution under in situ conditions and further enhancing their performance remain key challenges. Herein, we report that single-atom Mo doping in NiO significantly enhances its HER activity, reducing the overpotential to 131 mV at 10 mA cm −2 compared to undoped NiO. In situ X-ray absorption spectroscopy and Raman spectroscopy reveal that under catalytic conditions, Mo single atoms remain structurally stable, while Ni 2+ species in NiO are converted to Ni(OH) 2 in alkaline media under the applied working potential for HER. Notably, this transformation is absent in undoped NiO, indicating that Mo doping promotes the formation of active Ni(OH) 2 sites, which, in turn, accelerate the rate-limiting water dissociation step. These findings provide critical mechanistic insights into the structural evolution of NiMoO x during alkaline HER and highlight the importance of in situ studies in the development of highly efficient catalysts.

58 GEOSCIENCES↗
DOE OSTI2025

Impact of the d 0 transition metal on local structural transformations in disordered rock salt cathodes

Although it is widely accepted that the long-range (average) crystal structure plays a critical role in determining the electrochemical performance of battery materials, the relationship between local structural features and electrochemical performance is rarely studied. Disordered rock salt oxides (DRX), which have become serious contenders for next generation Li-ion electrode materials, provide an ideal platform for exploring correlations between local structure and electrochemical performance as they exhibit a simple face-centered cubic structure and combine long-range disorder and short-range order on the cation sublattice. This work examines the Li 1.1 Mn 0.7 Zr 0.2−x Ti x O 2 series of DRX cathodes and investigates the links between local structure rearrangements and capacity activation. The end-member Li 1.1 Mn 0.7 Zr 0.2 O 2 compound exhibits a low capacity in the as-synthesized state, attributed to unfavorable short-range order that hinders Li-ion transport, yet its capacity increases seven-fold, from 20 to 140 mAh g −1 , after chemical delithiation followed by a 400 °C heat treatment. Capacity activation is associated with the appearance of local spinel-like structural features that depart from the short-range order originally present in the material, without significant change to the bulk composition and average crystal structure. Investigation of a series of Li 1.1 Mn 0.7 Zr 0.2−x Ti x O 2 (x ≤ 0.2) DRX compounds reveals that the correlation length of the spinel-like ordering that emerges during the heat treatment strongly depends on the Zr : Ti ratio. Yet, dramatic capacity activation and electrochemical (pseudo-)plateaus reminiscent of Mn-based spinel cathodes are observed for all compounds irrespective of the size of the ordered domains. To explain this phenomenon, we propose that the DRX phase undergoes a complete transformation to a spinel-like domain structure, which improves bulk Li-ion transport regardless of domain size.

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

Gaunt and Breit two-electron contributions to mean-field transformations and fine structure splitting

Materials utilized by novel energy systems are often studied using weakly correlated mean-field theories. However, if these systems incorporate heavy elements, relativistic effects must be included. Therefore, a Kramers unrestricted coupled cluster with singles and doubles excitation formalism within a molecular mean-field exact two-component framework (X2C mmf ) using a four-component Dirac–Hartree–Fock (DHF) reference state is presented. The exact X2C mmf transformed normal-order Hamiltonian incorporates all one-electron and two-electron (2e) contributions from the Coulomb, Gaunt, and Breit operators and is used with the equation of motion method to calculate the excitation energies of the alkali group of elements. Using this framework, the effects of 2e Gaunt and Breit integrals are studied. Results demonstrate growing contributions from these integrals to the generated X2C mmf mean-fields and electronic fine structure calculations with increasing atomic number. Overall, this paper outlines the method, its effect within the X2C mmf approach, and lays the foundation for future theoretical development of relativistic calculations within this framework.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Stimulated geologic hydrogen: from mechanistic control to engineered rock transformation

Geologic hydrogen (GeoH 2 ) generated from subsurface iron-rich rock–water reaction (i.e., serpentinization) is emerging as a promising candidate for the next primary energy source. Yet, accelerating GeoH 2 production from geological to human timescales via enhanced serpentinization remains a formidable scientific and technical challenge. Here, in this Review, we decipher the mechanistic control and explore strategies for accelerating in situ, engineered iron-rich rock transformation into carbon-free GeoH 2 by orders of magnitude. Serpentinization rate is hindered by low porosity and permeability of source rocks, suboptimal temperatures, unfavorable water chemistry, inefficient Fe 2+ -to-Fe 3+ conversion, thermodynamic constraints, and low reactive surface area. While closed-system experiments provide valuable mechanistic insights, open-system conditions with fluid circulation are more crucial for economically viable GeoH 2 production. We assess stimulation techniques from enhanced hydrocarbon and geothermal recovery as tools to be adopted or adapted for increasing reactive surface areas for stimulated GeoH2 production. We estimate that 7.40 × 10 5 to 1.73 × 10 6 million metric tons (Mt) of hydrogen could be engineered over 20 to 50 years from about 10% iron-rich rocks within 10 km depth of continental crust. Enabling GeoH 2 as a viable energy source requires not only advancing scientific frontiers but also forming a global GeoH 2 research network and innovation ecosystem to address the critical scientific, technical, societal, economic, and policy challenges.

08 HYDROGEN↗
DOE OSTI2024

Pressure-induced transformation of Nb 2 O 5 under shock compression from first principles

Ab initio molecular dynamics (AIMD) simulations were carried out to investigate the equation of state of Nb 2 O 5 and its pressure-density relationship under shock conditions. Here, the focus of this study is on the monoclinic B-Nb 2 O 5 (C2/c) polymorph. Enthalpy calculations from AIMD trajectories at 300 K show that the pressure-induced transformation between the thermody-namically most stable crystalline monoclinic parent phase H-Nb 2 O 5 (P2/m) and B-Nb 2 O 5 occurs at ~1.9 GPa. This H→B transition is energetically more favorable than the H→L(Pmm2) pressure-induced transition recently observed at ~5.9-9.0 GPa. The predicted shock properties of Nb 2 O 5 polymorphs are also compared to their Nb and NbO 2 counterparts to assess the impact of niobium oxidation on shock response.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Accurate and efficient open-source implementation of domain-based local pair natural orbital (DLPNO) coupled-cluster theory using a t1-transformed Hamiltonian

We present an efficient, open-source formulation for coupled-cluster theory through perturbative triples with domain-based local pair natural orbitals [DLPNO-CCSD(T)]. Similar to the implementation of the DLPNO-CCSD(T) method found in the ORCA package, the most expensive integral generation and contraction steps associated with the CCSD(T) method are linear-scaling. In this work, we show that the t1-transformed Hamiltonian allows for a less complex algorithm when evaluating the local CCSD(T) energy without compromising efficiency or accuracy. Our algorithm yields sub-kJ mol−1 deviations for relative energies when compared with canonical CCSD(T), with typical errors being on the order of 0.1 kcal mol−1, using our TightPNO parameters. We extensively tested and optimized our algorithm and parameters for non-covalent interactions, which have been the most difficult interaction to model for orbital (PNO)-based methods historically. To highlight the capabilities of our code, we tested it on large water clusters, as well as insulin (787 atoms).

Chemistry↗