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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Non–linear bonding trends in maleonitrile-1,2–dithiolate complexes of the transuranium actinides

The trivalent actinides are produced in the nuclear fuel cycle during power production and provide the largest long-term radiation dose in used nuclear fuel. It is ideal for these elements to be removed from used nuclear fuel for disposal and a necessity for fuel recycling. A key challenge to this is the similarity of chemical behavior of the trivalent actinides to the lanthanides that are also present as fission products in used fuel. Thus far, some of the most effective separations of actinides from lanthanides utilise chelating agents containing sulfur moieties such as dithiophosphinates that selectively bind to actinide ions because of a greater bond covalency relative to lanthanide ions. Typically, greater differences between actinide and lanthanide ions are observable the more ligands and chelators bonds have a covalent character. Here, a series of complexes of the trivalent actinides Np(III) through Cf(III) (excluding Bk(III)) with maleonitrile-1,2-dithiolate (mnt 2– ) are synthesized along with their lanthanide counterparts (La(III) – Nd(III), Sm(III) – Gd(III), Dy(III)), in order to characterize the nature of chemical bonds with these metal ions and a polarizable, non-innocent, sulfur-donor ligand. The metal-sulfur bonds in these complexes trend shorter than measured for lanthanides with equivalent ionic radii. However, particularly large deviations are observed in the neptunium and plutonium complexes in both structure and bonding, resulting in a nonlinear bond length trendline for the actinide series. Density Functional Theory (DFT) calculations with Quantum Theory of Atoms in Molecules (QTAIM) and Natural Bond Order (NBO) analyses indicate that for the neptunium and plutonium complexes, the presence of increased 5f-orbital participation, energy degeneracy of the metal and ligand orbitals, and the structure packing result in shortened M–S bonds. The stabilization of the energy of the 5f-orbitals and the decrease in f-contribution to bonding orbitals in the later actinides results in structural properties more similar to the lanthanide complexes.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2026

Photoinduced correlations in stochastic dynamics of a solid-state ionic conductor

Photoexcitation by ultrashort laser pulses plays a crucial role in controlling reaction pathways, creating nonequilibrium material properties, and probing complex molecular dynamics. The photoresponse following a laser pulse is generally nonidentical between exposures due to spatiotemporal fluctuations or the stochastic nature of dynamical pathways. However, most ultrafast pump-probe experiments struggle to distinguish intrinsic sample fluctuations from extrinsic apparatus noise, often missing deviations from the averaged response. Leveraging the stability and high photon flux of time-resolved X-ray micro-diffraction at a synchrotron, we characterized stochastic photoinduced dynamics in a solid-state ionic conductor. By analyzing temporal evolutions of the lattice parameter of a single grain, we found that shot-to-shot fluctuations are not independent. Instead, correlations exist between nonequilibrium lattice trajectories following adjacent shots, with a characteristic correlation length of approximately 1500 shots, corresponding to an energy barrier of 0.4 ± 0.1 eV, close to the activation energy of lithium-ion diffusion.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Towards accurate prediction of configurational disorder properties in materials using graph neural networks

Abstract The prediction of configurational disorder properties, such as configurational entropy and order-disorder phase transition temperature, of compound materials relies on efficient and accurate evaluations of configurational energies. Previous cluster expansion methods are not applicable to configurationally-complex material systems, including those with atomic distortions and long-range orders. In this work, we propose to leverage the versatile expressive capabilities of graph neural networks (GNNs) for efficient evaluations of configurational energies and present a workflow combining attention-based GNNs and Monte Carlo simulations to calculate the disorder properties. Using the dataset of face-centered tetragonal gold copper without and with local atomic distortions as an example, we demonstrate that the proposed data-driven framework enables the prediction of phase transition temperatures close to experimental values. We also elucidate that the variance of the energy deviations among configurations controls the prediction accuracy of disorder properties and can be used as the target loss function when training and selecting the GNN models. The work serves as a fundamental step toward a data-driven paradigm for the accelerated design of configurationally-complex functional material systems.

Chemistry↗
DOE OSTI2025

TiSe 2 is a band insulator created by lattice fluctuations, not an excitonic insulator

TiSe 2 is a narrow-gap insulator with a rich array of unique properties. In addition to being a superconductor under certain modifications, it is commonly thought to be a rare realisation of an excitonic insulator. Below 200 K, TiSe 2 undergoes a transition from a high-symmetry ($P\bar{3}m1$) phase to a low-symmetry ($P\bar{3}c1$) charge density wave (CDW). Here we establish that it is indeed an insulator in both $P\bar{3}m1$ and $P\bar{3}c1$ phases. However, the insulating state is driven not by excitonic effects but by symmetry-breaking. In the CDW phase it is static. At high temperature, thermally driven instantaneous deviations from $P\bar{3}m1$ break the symmetry on the characteristic time scale of a phonon. Even though the time-averaged lattice structure assumes $P\bar{3}m1$ symmetry, the time-averaged energy band structure is closer to the CDW phase - a rare instance of a metal-insulator transition induced by dynamical symmetry breaking. We establish these conclusions from quasiparticle self-consistent GW (QSGW) and many-body calculations (QS$G\widehat{W}$), in combination with molecular dynamics simulations to capture the effects of thermal disorder. The many-body theory includes explicitly ladder diagrams in the polarizability, which incorporates excitonic effects in an ab initio manner. We find that the excitonic modification to the potential is weak, ruling out the possibility that TiSe 2 is an excitonic insulator.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Impact of microkinetic modeling assumptions on predicted kinetics and mechanisms over undercoordinated sites

Accurate modeling of catalytic reactions on undercoordinated sites requires accounting for the structural and ensemble-specific nature of the active sites. This study examines how common microkinetic modeling (MKM) assumptions affect predicted kinetics and mechanisms on the stepped Pt(211) facet for the ethane dehydrogenation (EDH) and the ethane hydrogenolysis (EH). Six (211) MKMs were developed, differing in (i) the number of active sites represented, (ii) adsorbate site occupancy treatment, and (iii) inclusion of cross-facet interactions. These models are benchmarked against a particle-based microkinetic model (PB-MKM), which best represents step-edge behavior. MKM assumptions caused deviations in turnover frequencies exceeding ten orders of magnitude and led to contrasting mechanistic and selectivity predictions. Multi-site MKMs overestimate activity by inflating free site availability, single-site models underestimate activity, and uniform occupancy models overpredict coverage of multi-dentate intermediates, leading to reaction-specific artifacts. Overall, the Combined Site Edge Model (CSEM), a single-site MKM accounting for site occupancy and cross-facet interactions, most closely approximates PB-MKM predictions. All models predict similar kinetics when surfaces are clean or primarily occupied by monodentate species. This work provides practical guidance for selecting MKM frameworks for undercoordinated catalytic surfaces and highlights the critical role of modeling assumptions in catalytic predictions.

(211) facet↗
DOE OSTI2026

Active learning enables generation of molecules that advance the known Pareto front

Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the properties of the structures represented in the training distribution. We find that this limitation arises not only from the molecule generation process itself, but also from the poor generalization capabilities of molecular property predictors. We address this challenge by creating a closed-loop molecule generation pipeline with iterative retraining on new quantum chemical simulation data. Compared against static, single-pass generative modeling approaches, only our closed-loop iterative workflow generates molecules with properties extending beyond the training distribution (up to 0.44 standard deviations beyond the original range) and achieves a 79% improvement in out-of-distribution molecule classification accuracy. Furthermore, by conditioning molecular generation on thermodynamic stability data obtained during the iterative loop, the proportion of stable and hence potentially synthesizable molecules generated is 3.5x higher than the next-best model.

Chemistry↗
DOE OSTI2026

Machine learning pipeline for denoising low signal-to-noise ratio and out-of-distribution transmission electron microscopy datasets

High-resolution transmission electron microscopy (HRTEM) is crucial for observing material’s structural and morphological evolution at Angstrom scales, but the electron beam can alter these processes. Devices such as CMOS-based direct-electron detectors operating in electron-counting mode can be utilized to substantially reduce the electron dosage. However, the resulting images often lead to a low signal-to-noise ratio, which requires frame integration that sacrifices temporal resolution. Several machine learning (ML) models have been recently developed to successfully denoise HRTEM images. Yet, these models are often computationally expensive, and their inference speeds on GPUs are outpaced by the imaging speed of advanced detectors, precluding in situ analysis. Furthermore, the performance of these denoising models on datasets with imaging conditions that deviate from the training datasets has not been evaluated. To mitigate these gaps, we propose a new self-supervised ML denoising pipeline specifically designed for time-series HRTEM images. This pipeline integrates a blind-spot convolution neural network with pre-processing and post-processing steps, including drift correction and low-pass filtering. Results demonstrate that our model outperforms various other ML and non-ML denoising methods in noise reduction and contrast enhancement, leading to improved visual clarity of atomic features. Additionally, the model is drastically faster than U-Net-based ML models and demonstrates excellent out-of-distribution generalization. The model’s computational inference speed is in the order of milliseconds per image, rendering it suitable for application in in-situ HRTEM experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Vortex-induced anomalies in the superconducting quantum interference patterns of topological insulator Josephson junctions

The superconducting quantum interference (SQI) patterns of Josephson junctions fabricated from hybrid structures that interface an s-wave superconductor with a topological insulator can be used to detect signatures of novel quasiparticle states. Here, we compare calculated and experimental SQI patterns obtained from hybrid junctions fabricated on cadmium arsenide, a two-dimensional topological insulator. The calculations account for the effects of Abrikosov (anti-) vortices in the superconducting contacts. They describe the experimentally observed deviations of the SQI from an ideal Fraunhofer pattern, including anomalous phase shifts, node lifting, even/odd modulations of the lobes, irregular lobe spacing, and an asymmetry in the positive/negative magnetic field. We also show that under a current bias, these vortices enter the electrodes even if there is no intentionally applied external magnetic field. The results show that Abrikosov vortices in the electrodes of the junctions can explain many of the observed anomalies in the SQI patterns of topological insulator Josephson junctions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Nanoscopic cross-grain cation homogenization in perovskite solar cells

Multiscale cation inhomogeneity has been a major hurdle in state-of-the-art formamidinium–caesium (FA–Cs) mixed-cation perovskites for achieving perovskite solar cells with optimal power conversion efficiencies and durability. Although the field has attempted to homogenize the overall distributions of FA–Cs in perovskite films from both plan and cross-sectional views, our understanding of grain-to-grain cation inhomogeneity and ability to tailor it—that is, spatially resolving the FA–Cs compositional difference between individual grains down to the nanoscale—are lacking. Here we reveal that as fundamental building blocks of a perovskite film, individual grains exhibit cationic compositions deviating from the prescribed ideal composition, severely limiting the interfacial optoelectronic properties and perovskite layer durability. This performance-limiting nanoscopic factor is linked to thermodynamic-driven morphological grooving, leading to a segmented surface landscape. At the grain triple junctions, grooves form nanoscale groove traps that hinder the mixing of solid-state cations across grains and thus retard inter-grain FA–Cs mixing. By rationally modulating the heterointerfacial energies, we reduced the depth of these nanoscale groove traps by a factor of three, significantly improving cation homogeneity. Perovskite solar cells with shallower nanoscale groove traps demonstrate enhanced power conversion efficiencies (25.62%) and improved stability under various standardized international protocols. In conclusion, our work highlights the significance of resolving surface nano-morphologies for homogeneous properties of perovskites.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Liquid-like dynamics in a solid-state lithium electrolyte

Superionic materials represent a regime intermediate between the crystalline and liquid states of matter. Despite the considerable interest in potential applications for solid-state batteries or thermoelectric devices, it remains unclear whether the fast ionic diffusion observed in superionic materials reflects liquid-like dynamics or whether the hops of mobile ions are inherently coupled to more conventional lattice phonons. Here we reveal a crossover from crystalline vibrations to relaxational dynamics of ionic diffusion in the superionic compound Li 6 PS 5 Cl, a candidate solid-state electrolyte. By combining inelastic and quasi-elastic neutron-scattering measurements with first-principles-based machine-learned molecular dynamics simulations, we found that the vibrational density of states in the superionic state strongly deviates from the quadratic behaviour expected from the Debye law of lattice dynamics. The superionic dynamics emerges from overdamped phonon quasiparticles to give rise to a linear density of states characteristic of instantaneous normal modes in the liquid state. Further, we showed that the coupling of lattice phonons with a dynamic breathing of the Li+ diffusion bottleneck enables an order-of-magnitude increase in diffusivity. Thus, our results shed insights into superionics for future energy storage and conversion technologies.

25 ENERGY STORAGE↗
DOE OSTI2025

Robust supermoiré pattern in large-angle single-twist bilayers

Forming long-wavelength moiré superlattices in van der Waals bilayers that have a small-angle twist between the two layers has been a key approach for creating moiré flat bands. However, for small twist angles, strong lattice reconstruction creates domain walls and other forms of disorder in the moiré pattern, posing considerable challenges for engineering such platforms. At large twist angles, the lattices are more rigid, but it is difficult to produce flat bands in shorter-wavelength moiré superlattices. Here, in this study, we introduce an approach for tailoring robust supermoiré structures in bilayers of transition-metal dichalcogenides using only a single twist near a commensurate angle. Structurally, we show the spontaneous formation of a periodic arrangement of three inequivalent commensurate moiré stackings, where the angle deviation from the commensurate angle determines the periodicity. Electronically, we reveal a large set of van Hove singularities that indicate strong band hybridization, leading to flat bands near the valence band maximum. Our study extends the study of the interplay among band topology, quantum geometry and moiré superconductivity to the large twist angle regime.

electronic properties and materials↗
DOE OSTI2024

Observation of quantum entanglement with top quarks at the ATLAS detector

Abstract Entanglement is a key feature of quantum mechanics 1–3 , with applications in fields such as metrology, cryptography, quantum information and quantum computation 4–8 . It has been observed in a wide variety of systems and length scales, ranging from the microscopic 9–13 to the macroscopic 14–16 . However, entanglement remains largely unexplored at the highest accessible energy scales. Here we report the highest-energy observation of entanglement, in top–antitop quark events produced at the Large Hadron Collider, using a proton–proton collision dataset with a centre-of-mass energy of √s = 13 TeV and an integrated luminosity of 140 inverse femtobarns (fb) −1 recorded with the ATLAS experiment. Spin entanglement is detected from the measurement of a single observableD, inferred from the angle between the charged leptons in their parent top- and antitop-quark rest frames. The observable is measured in a narrow interval around the top–antitop quark production threshold, at which the entanglement detection is expected to be significant. It is reported in a fiducial phase space defined with stable particles to minimize the uncertainties that stem from the limitations of the Monte Carlo event generators and the parton shower model in modelling top-quark pair production. The entanglement marker is measured to beD = −0.537 ± 0.002 (stat.) ± 0.019 (syst.) for$$340\,{\rm{GeV}} < {m}_{t\bar{t}} < 380\,{\rm{GeV}}$$ 340 GeV < m t t ¯ < 380 GeV . The observed result is more than five standard deviations from a scenario without entanglement and hence constitutes the first observation of entanglement in a pair of quarks and the highest-energy observation of entanglement so far.

Science & Technology - Other Topics↗
DOE OSTI2024

Imaging shapes of atomic nuclei in high-energy nuclear collisions

Atomic nuclei are self-organized, many-body quantum systems bound by strong nuclear forces within femtometre-scale space. These complex systems manifest a variety of shapes, traditionally explored using non-invasive spectroscopic techniques at low energies. However, at these energies, their instantaneous shapes are obscured by long-timescale quantum fluctuations, making direct observation challenging. Here we introduce the collective-flow-assisted nuclear shape-imaging method, which images the nuclear global shape by colliding them at ultrarelativistic speeds and analysing the collective response of outgoing debris. This technique captures a collision-specific snapshot of the spatial matter distribution within the nuclei, which, through the hydrodynamic expansion, imprints patterns on the particle momentum distribution observed in detectors. We benchmark this method in collisions of ground-state uranium-238 nuclei, known for their elongated, axial-symmetric shape. Our findings show a large deformation with a slight deviation from axial symmetry in the nuclear ground state, aligning broadly with previous low-energy experiments. This approach offers a new method for imaging nuclear shapes, enhances our understanding of the initial conditions in high-energy collisions and addresses the important issue of nuclear structure evolution across energy scales.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Strong electron–phonon coupling in magic-angle twisted bilayer graphene

The unusual properties of superconductivity in magic-angle twisted bilayer graphene (MATBG) have sparked considerable research interest. However, despite the dedication of intensive experimental efforts and the proposal of several possible pairing mechanisms, the origin of its superconductivity remains elusive. Here, by utilizing angle-resolved photoemission spectroscopy with micrometre spatial resolution, we reveal flat-band replicas in superconducting MATBG, where MATBG is unaligned with its hexagonal boron nitride substrate. These replicas show uniform energy spacing, approximately 150 ± 15 meV apart, indicative of strong electron–boson coupling. Strikingly, these replicas are absent in non-superconducting twisted bilayer graphene (TBG) systems, either when MATBG is aligned to hexagonal boron nitride or when TBG deviates from the magic angle. Calculations suggest that the formation of these flat-band replicas in superconducting MATBG are attributed to the strong coupling between flat-band electrons and an optical phonon mode at the graphene K point, facilitated by intervalley scattering. These findings, although they do not necessarily put electron–phonon coupling as the main driving force for the superconductivity in MATBG, unravel the electronic structure inherent in superconducting MATBG, thereby providing crucial information for understanding the unusual electronic landscape from which its superconductivity is derived.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Low-overhead transversal fault tolerance for universal quantum computation

Fast, reliable logical operations are essential for realizing useful quantum computers. By redundantly encoding logical qubits into many physical qubits and using syndrome measurements to detect and correct errors, we can achieve low logical error rates. However, for many practical quantum error correction codes such as the surface code, owing to syndrome measurement errors, standard constructions require multiple extraction rounds—of the order of the code distance d—for fault-tolerant computation, particularly considering fault-tolerant state preparation. Here we show that logical operations can be performed fault-tolerantly with only a constant number of extraction rounds for a broad class of quantum error correction codes, including the surface code with magic state inputs and feedforward, to achieve ‘transversal algorithmic fault tolerance’. Through the combination of transversal operations7 and new strategies for correlated decoding, despite only having access to partial syndrome information, we prove that the deviation from the ideal logical measurement distribution can be made exponentially small in the distance, even if the instantaneous quantum state cannot be made close to a logical codeword because of measurement errors. We supplement this proof with circuit-level simulations in a range of relevant settings, demonstrating the fault tolerance and competitive performance of our approach. Furthermore, our work sheds new light on the theory of quantum fault tolerance and has the potential to reduce the space–time cost of practical fault-tolerant quantum computation by over an order of magnitude.

Zhou, Hengyun [QuEra Computing, Boston, MA (United↗
DOE OSTI2026

More concentrated precipitation decreases terrestrial water storage

Terrestrial water availability is a key determinant of human and ecosystem well-being. Apart from mean precipitation and evaporation changes, it is unknown how daily-scale precipitation concentration into fewer, heavier events affects hydrologic partitioning and the land water balance. Here we show observationally that more concentrated precipitation decreases land water availability across all climates globally, a drying effect as strong in magnitude as the wetting effect of increased total precipitation. Simple and complex land-surface models recover the observed effect, whereas idealized simulations show that it arises from enhanced evaporation caused by hydrologic partitioning changes at the land surface. Projected terrestrial water storage impacts of warming-driven precipitation concentration at about 2 °C of warming shift the land surface to abnormally dry conditions (≥0.5 standard deviation) for 27% of the global population, independent of any total precipitation or irrigation changes. Our results show new key determinants of the land water balance, highlighting its sensitivity to the temporal distribution of precipitation, with broad implications for future water availability.

climate change↗
DOE OSTI2025

Advantages of imperfect dice rolls over coin flips for random number generation

With an eye toward neural-inspired probabilistic computation, recent work has examined the development of true random number generators via stochastic devices. Typically, these devices are operated in a two-state regime to produce a sequence of binary outcomes (i.e., coin flips). However, there is no guarantee that stochastic devices will infallibly produce fair outputs and small deviations from a uniform distribution may have unwanted complications in applications. Using mathematical analysis, we contend that opting instead for a multi-state device (i.e., a dice roll) has benefits in these unfair paradigms. To demonstrate these benefits, we apply this framework to the analysis of a tunnel diode operated in a stochastic regime. In particular, interpreting the binary stochastic output of the tunnel diode as a multi-state die roll output also sees advantages in remaining closer to uniform. Overall, our approach provides a compelling argument for mathematical driven co-design and development of novel probabilistic computing devices and hardware.

applied mathematics↗
DOE OSTI2026

Investigating property-porosity relationships for micro-architected lattice structures

Micro-architected structures are increasingly valued for their light weight and tunable mechanical properties; this class of material includes sheet structures like the Schöen gyroid and beam lattices like the octet. For design purposes, it is critical to understand how to tune the relative density (RD) to obtain desired mechanical properties. This study investigates the mechanical response of Ti-6Al-4V gyroid structures, spanning a broad range of RDs (0.03–0.90), unit cell sizes (1–4 cm), and sheet thicknesses (0.2–7.8 mm). Results demonstrate that the classical Gibson-Ashby power law scaling between RD and modulus and yield stress does not adequately capture the response over a wide range of RDs, nor does it extrapolate correctly to the fully dense solid. Analytical models, in concert with experimental results and high-fidelity finite element calculations, show that the deviation from Gibson-Ashby reflects a transition from structure-dominated to material-dominated behavior. Distinctions are drawn between scaling relationships and property-porosity models, and an analytical model is proposed to better capture the evolution of mechanical properties with RD. These observations are mirrored in other architected topologies, like the octet, and highlight the importance of understanding the relationship between mechanical properties and geometry for design purposes.

Materials science↗