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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Combining Theory and Experiment to Map the Atomic-Level Structure–Energy Pathways of Adsorbate-Mediated Phase Changes in a Cooperatively Flexible Metal–Organic Framework

An important subclass of metal–organic frameworks (MOFs) exhibits cooperative flexibility, wherein individual crystallites undergo global structural phase changes in response to external stimuli. Where cooperative flexibility results in reversible changes between crystalline states of distinct accessible porosity, these frameworks can exhibit rare yet desirable behaviors that cannot be explained by local dynamics alone. Yet, the chemical and structural origins of cooperative flexibility and how frameworks undergo these reversible phase changes at the atomic level remain poorly understood. Deliberate design for specific applications is therefore exceedingly difficult, and there is great impetus to develop a fundamental understanding of this phenomenon. Here, an effective and widely accessible computational approach is developed, which is designed to provide microscopic resolution via direct comparison to experimental data along the desorption-guided pathway. The strategy is applied to explain the desorption-induced phase change in an experimentally well-characterized framework, CdIF-13 (sod-Cd(benzimidazolate)2), where experiment alone was unable to resolve the atomistically detailed phase change landscape. Our findings reveal that the cooperative phase change pathways are adsorbate dependent with thermodynamics of intermediate structural states dictated by a nuanced interplay of ligand orientation, skeletal symmetry, and modes of surface adsorption. The results reveal that this isotropically flexible framework is “chaperoned” through a complex energy landscape by specific adsorbates, revealed by the reported computational approach with atomic-level insight and validated by experimentally determined structures. Thus, this work facilitates both understanding and future design of flexible materials for applications in gas storage, transport, delivery, and separation technologies.

03 NATURAL GAS↗
DOE OSTI2026

Deciphering the Solvation Structure of Aqueous ZnCl 2 Solutions from X-ray Absorption Spectra Using the Interpretable Graph Neural Network

Machine learning (ML) provides powerful pathways for predicting spectroscopic observables from atomic structures, but its broader impact depends on making model predictions interpretable in terms of physical and chemical principles. Here, we introduce a physics-guided graph neural network (GNN) model that predicts Zn K-edge X-ray spectroscopy (XAS) spectra of aqueous ZnCl 2 solutions. Training data are generated from ab initio XAS calculations on molecular dynamics snapshots obtained using a machine learning interatomic potential. The GNN reproduces experimental spectra across concentrations from dilute (<0.1 m) to highly concentrated (30 m, “water-in-salt”) regimes and scales efficiently to large, disordered liquid systems beyond the reach of conventional ab initio approaches. Gradient-based attribution analysis reveals that the model learns physically meaningful structure-spectrum relationships. Ligand-specific attributions reflect orbital hybridization patterns and the origin of the excitations derived from the density functional theory. Bond-length attributions recover spectral shifts consistent with multiple-scattering theory. Finally, this work bridges data-driven prediction with electronic-structure theory, establishing a general paradigm for interpretable ML that links atomic structure, electronic structure, and spectroscopic observables.

25 ENERGY STORAGE↗
DOE OSTI2024

Exploring Structural Anisotropy in Amorphous Tb-Co via Changes in Medium-Range Ordering

Amorphous thin films grown by magnetron co-sputtering exhibit changes in atomic structure with varying growth and annealing temperatures. Structural variations influence the bulk properties of the films. Scanning nanodiffraction performed in a transmission electron microscope (TEM) is applied to amorphous Tb 17 Co 83 (a-Tb-Co) films deposited over a range of temperatures to measure relative changes in medium-range ordering (MRO). These measurements reveal an increase in MRO with higher growth temperatures and a decrease in MRO with higher annealing temperatures. The trend in MRO indicates a relationship between the growth conditions and local atomic ordering. By tilting select films, the TEM measures variations in the local atomic structure as a function of orientation within the films. The findings support claims that preferential ordering along the growth direction results from temperature-mediated adatom configurations during deposition, and that oriented MRO correlates with increased structural anisotropy, explaining the strong growth-induced perpendicular magnetic anisotropy found in rare earth–transition metal films. Beyond magnetic films, we propose the tilted FEM workflow as a method of extracting anisotropic structural information in a variety of amorphous materials with directionally dependent bulk properties, such as films with inherent bonding asymmetry grown by physical vapor deposition.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Elucidation of Local Ordering and Atomic-Scale Structure in Polymer-Derived SiOC

Silicon oxycarbide (SiOC) is a versatile ceramic material with tunable microstructure and compositions that can be modulated through precursor chemistry and processing conditions. Though there are several noteworthy uses of SiOC across a range of application spaces, the difficulties in elucidating the short- to medium-range order within these materials have limited the maturation of strategies to precisely control SiC x O 4–x compositions for user-tailored applications. In this contribution, we implement a range of synchrotron scattering and spectroscopy methods coupled with stochastic modeling techniques to elucidate changes in local chemistry and structure associated with the pyrolysis of a commercially available SiOC polymer precursor. Stochastic modeling approaches provide valuable insights into decoupling local Si–O and Si–C environments while confirming predominate heterogeneous phases in materials. Using pyrolysis temperatures between 250 to 800 °C results in a heterogeneous material predominately composed of SiOC and amorphous SiO 2 domains. At 1100 °C, redistribution of Si–C pairs in the SiOC network and Si–O from the SiO 2 domains create a more ordered SiOC phase with local cubic SiC-like ordering. In addition, residual carbon leads to a detectable carbon phases at 1100 °C that persist at higher temperatures. These efforts address the difficulties of obtaining atomic-scale insights into the local structure and nanoscale heterogeneities in SiOC, providing pathways toward establishing structure–property relationships for future materials development.

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

A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)

Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Status Report on the Development of Cladding Inner-wall and Outer-wall Coatings

Advanced cladding is critical for nuclear reactors with enhanced performance in radiation tolerance, neutron transparency and reactor safety. Using advanced cladding will ensure the adequate thermal conductivity and mechanical stability of the cladding base material, corrosion resistance and high-temperature coolant compatibility of the cladding surface, and chemical stability in the cladding inner wall against fuel cladding chemical interaction (FCCI). An innovative cladding with a three-layer structure (i.e., a modified outer surface, a robust clad base material, and a modified inner wall) promises to meet all these requirements. In this status report, the initial efforts on the development of cladding inner-wall and outer-wall coatings are summarized. The activities covered in this report are (1) High-resolution TEM studies of TiN/HT-9 interface atomic structure and chemistry after ion irradiation, (2) High-resolution TEM studies of TiN/14YWT ODS interface atomic structure after thermal cycling, (3) Inner-wall TiN coating for an experimental 14YWT ODS cladding, and (4) Corrosion test of TiN coated Ziracloy-4 bar samples.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Identification of Defects and the Origins of Surface Noise on Hydrogen–Terminated (100) Diamond

Near-surface nitrogen vacancy centres are critical to many diamond-based quantum technologies such as information processors and nanosensors. Surface defects play an important role in the design and performance of these devices. The targeted creation of defects is central to proposed bottom-up approaches to nanofabrication of quantum diamond processors, and uncontrolled surface defects may generate noise and charge trapping which degrade shallow NV device performance. Surface preparation protocols may be able to control the production of desired defects and eliminate unwanted defects, but only if their atomic structure can first be conclusively identified. This work uses a combination of scanning tunnelling microscopy (STM) imaging and first-principles simulations to identify several surface defects on H:C(100)—2 × 1 surfaces prepared using chemical vapour deposition (CVD). The atomic structure of these defects is elucidated, from which the microscopic origins of magnetic noise and charge trapping are determined based on the modeling of their paramagnetic properties and acceptor states. Rudimentary control of these deleterious properties is demonstrated through STM tip-induced manipulation of the defect structure. Furthermore, the results validate accepted models for CVD diamond growth by identifying key adsorbates responsible for the nucleation of new layers.

36 MATERIALS SCIENCE↗
DOE OSTI2025

CuXASNet: Rapid and accurate prediction of copper L-edge x-ray absorption spectra using machine learning

In this work, we have developed CuXASNet, a dense neural network that predicts simulated Cu -edge x-ray absorption spectra (XAS) from atomic structures. Featurization of the Cu local environment is performed using a component of M3GNet, a graph neural network developed for predicting the potential energy surface. CuXASNet is trained on simulated spectra from FEFF9 at the multiple scattering level of theory, and can predict the and edges for Cu sites to quantitative accuracy. To validate our approach, we compare 14 experimental spectra extracted from the literature with the predictions of CuXASNet. The agreement of CuXASNet with experiments is shown by an average mean absolute error of 0.125 and an average Spearman's correlation coefficient of 0.891, which is comparable to FEFF9's values of 0.131 and 0.898 for the same metrics. As such, CuXASNet can rapidly predict a large number of -edge XAS spectra at the same accuracy as FEFF9 simulations. This can be used as a drop-in replacement for multiple scattering codes for fast screening of candidate atomic structure models of a measured system. This model establishes a general framework for Cu XAS prediction, and can be extended to more computationally expensive levels of theory and to other transition metal edges.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unravelling Disorder in Aperiodic Crystals – Diffuse Scattering and Atomic Resolution Holography

The atomic–scale disorder of aperiodic crystals, and quasicrystals in particular, is inherently difficult to explore by experimental methods due to their complex atomic arrangements. Two advanced characterization techniques, a revived and an emerging one, offer direct experimental access even to such complex atomic structures: Diffuse Scattering and Atomic Resolution Holography. Finally, in this overview, we introduce their specific application to aperiodic crystals and discuss their merits and difficulties.

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

Real-Time Atomic-Scale Structural Analysis Resolves the Amorphous to Crystalline CaCO 3 Mechanism Controversy

Amorphous calcium carbonate (ACC) occurs as a precursor to geological and biogenic calcium carbonate (CaCO 3 ), yet its transformation pathways and reaction mechanisms remain inconsistent and controversial. In this study, we investigated the transformation of ACC to calcite under both solution and dry conditions, in the presence and absence of impurity ions, utilizing operando time-resolved synchrotron X-ray diffraction (TRXRD) and reactive transport modeling. Results demonstrate that TRXRD techniques allow us to differentiate dissolution-reprecipitation versus solid-state transformation mechanisms for amorphous to crystalline phase transitions. Specifically, we observe that in environments with abundant water, ACC transforms to calcite through a dissolution-reprecipitation mechanism. This features an activation energy of 63 ± 2 kJ/mol and unit cell volume contraction during calcite crystal growth. Conversely, under water-limited conditions, ACC to calcite transformation proceeds via a solid-state transformation mechanism, with an activation energy of 210 ± 2 kJ/mol, three times greater than the dissolution-reprecipitation route, and a unit cell expansion during crystalline calcite growth. Further, to illustrate the magnitude of these effects, the rates of calcite growth were similar during dissolution-reprecipitation at 3 °C [0.00207(35) s –1 ] and solid-state transformation at 280 °C [0.00134(11) s –1 ]. Moreover, the incorporation of an impurity, strontium, significantly retards the rate of calcite growth while expanding its unit cell but whose incorporation is history dependent. Reactive transport modeling of the dissolution–precipitation kinetics suggests that ACC must be dissolving as compact aggregates. These various transformation mechanisms drive diverse geological and biological carbonate formations, impacting their use as paleoenvironmental markers and functional materials synthesis.

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

Atomic-scale 3D structural dynamics and functional degradation of Pt alloy nanocatalysts during the oxygen reduction reaction

Pt-based electrocatalysts are the primary choice for fuel cells due to their superior oxygen reduction reaction (ORR) activity. To enhance ORR performance and durability, extensive studies have investigated transition metal alloying, doping, and shape control to optimize the three key governing factors for ORR: geometry, local chemistry, and strain of their surface and subsurface. However, systematic optimization remains incomplete, as it requires an atomic-scale understanding of these factors and their dynamics over potential cycling, as well as their relationship to ORR activity. Here, we implement neural network-assisted atomic electron tomography to measure the 3D atomic structural dynamics and their effects on the functional degradation of PtNi alloy catalysts. Our results reveal that PtNi catalysts undergo shape changes, surface alloying, and strain relaxation during cycling, which can be effectively mitigated by Ga doping. By combining geometry, local chemistry, and strain analysis, we calculated the changes in ORR activity over thousands of cycles and observed that Ga doping leads to higher initial activity and greater stability. These findings offer a pathway to understanding 3D atomic structural dynamics and their relation to ORR activity during cycling, paving the way for the systematic design of durable, high-efficiency nanocatalysts.

Jeong, Chaehwa↗
DOE OSTI2025

Mapping strain and structural heterogeneities around bubbles in amorphous ionically conductive Bi 2 O 3

While amorphous materials are often approximated to have a statistically homogeneous atomic structure, they frequently exhibit localized structural heterogeneity that challenges simplified models. This study uses 4D scanning transmission electron microscopy to investigate the strain and structural modifications around gas bubbles in amorphous Bi 2 O 3 induced by argon irradiation. We present a method for determining strain fields surrounding bubbles that can be used to measure the internal pressure of the gas. Compressive strain is observed around the cavities, with higher-order crystalline symmetries emerging near the cavity interfaces, suggesting paracrystalline ordering as a result of bubble coarsening. This ordering, along with a compressive strain gradient, indicates that gas bubbles induce significant localized changes in atomic packing. By analyzing strain fields with maximum compressive strains of 3%, we estimate a lower bound on the internal pressure of the bubbles at 2.5 GPa. These findings provide insight into the complex structural behavior of amorphous materials under stress, particularly in systems with gas inclusions, and offer new methods for probing the local atomic structure in disordered materials. Although considering structural heterogeneity in amorphous systems is non-trivial, these features have crucial impacts on material functionalities, such as mechanical strength, ionic conductivity, and electronic mobility.

36 MATERIALS SCIENCE↗
DOE OSTI2025

CSGL: chemical synthesis graph learning for molecule representation

Abstract Motivation Molecule representation learning (MRL) translates molecules into a real vector space, serving as input to downstream tasks in biology, chemistry, and computer science. This article introduces a chemical synthesis graph learning (CSGL) framework, which enhances MRL by considering both the atomic structures of molecules and their roles in chemical reactions through a hierarchical graph representation. Specifically, molecules are first modeled based on their molecular graphs, which capture atomic-level structural information. They are then further refined using a chemical synthesis graph, where nodes represent reactant and product molecule sets, and edges encode chemical transformations between reactants and products (e.g. changes in molecular structures). CSGL optimizes molecular embeddings of reactant and product nodes in a fashion that ensures the embeddings conform to a chemical balance constraint. Results Experimental results show that our method CSGL achieves strong performance on a variety of tasks, including product prediction, reaction classification, and molecular property prediction. Availability and implementation https://github.com/li-2023/CSGL.

Biochemistry & Molecular Biology↗
DOE OSTI2024

X-ray and molecular dynamics study of the temperature-dependent structure of molten NaF-Zr⁢F 4

The local atomic structure of NaF-Zr⁢F 4 (53–47 mol%) molten system and its evolution with temperature are examined with x-ray scattering measurements which are then used to validate the quality of ab initio and neural network-based molecular dynamics (NNMD) calculations in the temperature range 515–700°⁢C. The machine-learning enhanced NNMD calculations offer improved efficiency while maintaining accuracy at higher distances compared to ab initio calculations. Looking at the evolution of the pair distribution function with increasing temperature, a fundamental change in the liquid structure within the selected temperature range, accompanied by a slight decrease in overall correlation is revealed. NNMD calculations indicate the coexistence of three different fluorozirconate complexes: [Zr⁢F 6 ] 2– , [Zr⁢F 7 ] 3– , and [Zr⁢F 8 ] 4– , with a shift in the dominant coordination state from the 7-coordinated Zr cation toward a 6-coordinated cation with increasing temperature. The study also highlights the metastability of different local coordination structures, with frequent interconversions between the 6- and 7-coordinate states. Analysis of the Zr-F-Zr angular distribution function reveals the presence of both “edge-sharing” and “corner-sharing” fluorozirconate complexes with specific bond angles and distances in accord with previous studies, while the next-nearest-neighbor cation-cation correlations demonstrate a clear preference for unlike cations as nearest-neighbor pairs, emphasizing nonrandom arrangement. Finally, these findings contribute to a comprehensive understanding of the complex local structure of the molten salt, providing insights into temperature-dependent preferences and correlations within the molten system.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Bulk Stoichiometry-Controlled Surface Reconstruction of Nanosized Ni−In Intermetallic Catalysts Steers Methanol Selectivity in CO2 Hydrogenation

Intermetallic compounds (IMCs) are attractive platforms for elucidating structure−catalysis relationships due to their ordered atomic structure and well-defined bulk composition. Yet, how their surfaces reconstruct under reaction conditions and how such reconstruction is governed by bulk stoichiometry remain poorly understood. Here, we show that SiO2-supported Ni−In IMCs undergo reaction-driven surface reconstruction during CO2 hydrogenation and that bulk stoichiometry can be used to steer this evolution toward methanol formation. Among the compositions examined (Ni2In1, Ni1In1, Ni2In3, and Ni1In2), Ni2In3/SiO2 exhibits the highest methanol selectivity (∼70%) and a methanol space-time yield of 652 mg·gmetal−1·h−1 at 250 °C and 30 bar. Combined structural, surface characterization, and kinetic analyses suggest that the intermetallic bulk remains largely preserved, whereas the surface departs from the stoichiometric bulk and evolves toward InOx-enriched surface domains coupled to an electron-rich Ni−In intermetallic phase. The extent of this evolution depends strongly on the bulk Ni:In stoichiometry and is most pronounced for Ni2In3/SiO2. These findings identify bulk stoichiometry as a handle for tuning the working-state surface of intermetallic catalysts and provide a basis for designing methanol synthesis catalysts through controlled surface reconstruction.

Wang, Caiqi [ORNL] (ORCID:0000000198849990)↗
DOE OSTI2025

Amorphous zinc–molybdenum–sulfide chalcogel as a long-cycle, high-capacity electrode for lithium-ion batteries

The inherent limitations of intercalation-based electrodes in lithium-ion batteries have prompted the search for alternative materials with higher specific capacities and robust electrochemical stability. Sulfur-based electrodes, despite their high theoretical capacities (1672 mAh g −1 ), typically suffer from poor cycling performance. In this work, zinc molybdenum polysulfide (Zn x Mo 3 S 13 , 0.5 ≤ x), an amorphous semiconductor chalcogel, exhibits high specific capacity and excellent cycling stability. Synchrotron X-ray pair distribution function and extended X-ray absorption fine structure analyses reveal a short-range atomic structure comprising Mo–Mo, M–S (M = Mo, Zn), and S–S bonding motifs. The coordination environment of Mo and S closely resembles that of Mo 3 S 13 clusters, interconnected via S–S bridges and Zn 2+ cations. The Li/Zn x Mo 3 S 13 cell delivers an initial discharge capacity of 844 mAh g −1 at C/3, and retains 386.2 mAh g −1 after 1000 cycles with an average coulombic efficiency of 99.99%. The distribution of relaxation times analysis confirms the formation of a stable solid electrolyte interphase, which underpins the cell's long-term stability. In conclusion, this outstanding performance is attributed to the synergistic effects of the chalcogel's unique amorphous framework, semiconductive character, Zn-mediated polysulfide anchoring, and structural resilience, positioning Zn x Mo 3 S 13 chalcogel among the most durable pure metal sulfide cathodes reported for next-generation LIBs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Simulated structure and thermodynamics of decagonal Al-Co-Cu quasicrystals

Atomic structures of Al-Co-Cu decagonal quasicrystals (dQCs) are investigated using empirical oscillating pair potentials (EOPP) in molecular dynamic (MD) simulations that we enhance by Monte Carlo (MC) swapping of chemical species and replica exchange. Predicted structures exhibit planar decagonal tiling patterns and are periodic along the perpendicular direction. We then recalculate the energies of promising structures using first-principles density functional theory (DFT), along with energies of competing phases. We find that our τ -inflated sequence of QC approximants (QCAs) are energetically unstable at low temperature by at least 3 meV/atom. Extending our study to finite temperatures by calculating harmonic vibrational entropy, as well as anharmonic contributions that include chemical species swaps and tile flips, our results suggest that the quasicrystal phase is entropically stabilized at temperatures in the range 600-800 K and above. It decomposes into ordinary (though complex) crystal phases at low temperatures, including a partially disordered B2-type phase. We discuss the influence of density and composition on QC phase stability; we compare the structural differences between Co-rich and Cu-rich quasicrystals; and we analyze the role of entropy in stabilizing the quasicrystal, concluding with a discussion of the possible existence of “high entropy” quasicrystals. Published by the American Physical Society 2024

Huang, Yang (ORCID:0009000045917347)↗
DOE OSTI2025

Medium-range order and compositional correlation in metallic glasses

The compositional atomic ordering in metallic glasses was studied by simulation focusing on the medium-range order (MRO). Many metallic alloy liquids and glasses show MRO characterized by the oscillations in the atomic pair-distribution function (PDF) beyond the first peak, which decay exponentially with distance. To study the effects of the local chemical order on MRO, we examine the compositionally resolved PDF and its MRO for models of various binary metallic alloy glasses. We show that compositional ordering is limited mostly to the nearest-neighbor atoms and the MRO is largely independent of the compositional order. For some elements that strongly repel each other in the alloy, a second MRO periodicity is observed owing to the distinct correlations among them. These results are discussed in light of the idea that the MRO oscillations in the PDF describe the correlations in the atomic density fluctuations, rather than the detailed local atomic structure.

Atomic structure↗