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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Thermodynamics of high-pressure ice phases explored with atomistic simulations

Most experimentally known high-pressure ice phases have a body-centred cubic (bcc) oxygen lattice. Our large-scale molecular-dynamics simulations with a machine-learning potential indicate that, amongst these bcc ice phases, ices VII, VII' and X are the same thermodynamic phase under different conditions, whereas superionic ice VII" has a first-order phase boundary with ice VII'. Moreover, at about 300 GPa, the transformation between ice X and the Pbcm phase has a sharp structural change but no apparent activation barrier, whilst at higher pressures the barrier gradually increases. Our study thus clarifies the phase behaviour of the high-pressure ices and reveals peculiar solid–solid transition mechanisms not known in other systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Capturing dynamical correlations using implicit neural representations

Abstract Understanding the nature and origin of collective excitations in materials is of fundamental importance for unraveling the underlying physics of a many-body system. Excitation spectra are usually obtained by measuring the dynamical structure factor, S ( Q , ω ), using inelastic neutron or x-ray scattering techniques and are analyzed by comparing the experimental results against calculated predictions. We introduce a data-driven analysis tool which leverages ‘neural implicit representations’ that are specifically tailored for handling spectrographic measurements and are able to efficiently obtain unknown parameters from experimental data via automatic differentiation. In this work, we employ linear spin wave theory simulations to train a machine learning platform, enabling precise exchange parameter extraction from inelastic neutron scattering data on the square-lattice spin-1 antiferromagnet La 2 NiO 4 , showcasing a viable pathway towards automatic refinement of advanced models for ordered magnetic systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Nanoscale wetting controls reactive Pd ensembles in synthesis of dilute PdAu alloy catalysts

The performance of bimetallic dilute alloy catalysts is largely determined by the size of minority metal ensembles on the nanoparticle surface. By analyzing the synthesis of catalysts comprising Pd 8 Au 92 nanoparticles supported on silica using surface-sensitive techniques, we report that whether Pd overgrowth occurs before or after Au nanoparticle deposition onto the support controls the surface Pd ensemble size and abundance. These differences in Pd ensembles influence catalytic reactivity in H 2 –D 2 isotope exchange and benzaldehyde hydrogenation, which, in correlation with theoretical calculations, is used to elucidate the active site(s) in each reaction. To clarify how the synthetic sequence controls the formation of Pd ensembles, we combine numerical wetting calculations and molecular dynamics simulations (with a machine-learned force field) to visualize Pd deposition and migration on the nanoparticle surface, respectively. Our results suggest that the nanoparticle–support interface restricts nanoparticle accessibility to Pd deposition, which consequently controls the Pd ensemble size, illustrating the critical role of nanoscale wetting phenomena during bimetallic catalyst preparation.

36 MATERIALS SCIENCE↗
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 OSTI2026

Equilibrium and non-equilibrium effects in high pressure phase transformations of carbon

The behavior of carbon in the range 1–100 GPa and 1–10 kK is central to problems in planetary interiors, inertial confinement fusion targets, and high-pressure synthesis of carbon-based materials, but experiments in this regime are difficult and often provide only indirect constraints on phase behavior. As a result, phase boundary loci, structure, and limits of metastability at high pressure remain uncertain. In this work, machine-learning enhanced atomistic simulations are used to address this knowledge gap. We determine the melt line up to 100 GPa, the graphite-diamond phase boundary up to the melt line, and analyze structure of the coexisting phases. We show that the coexisting liquid evolves smoothly with pressure without evidence for a first-order liquid–liquid transition. Orientation-resolved graphite melting simulations indicate that basal-plane interfaces develop a dewetting layer and undergo layer-by-layer melting, producing kinetic hysteresis and an apparent orientation dependence of the melt line. Non-equilibrium quenches from the melt are used to construct a kinetically limiting graphite–diamond phase boundary for rapid quenches from above the melt line, and show that graphite is metastable at pressures of up to ≈ 25 GPa. These results provide bounds on equilibrium and metastable behavior in carbon relevant for interpreting high-pressure experiments and for designing synthesis pathways to specific carbon microstructures.

Lyu, Yanjun [Department of Materials Science and E↗
DOE OSTI2021

Fast Na diffusion and anharmonic phonon dynamics in superionic Na 3 PS 4

The design of new solid electrolytes (SEs) hinges on identifying and tuning relevant descriptors. Phonons describe the atomic dynamics in crystalline materials and provide a basis to encode possible minimum energy pathways for ion migration, but anharmonic effects can be large in SEs. Identifying and controlling the pertinent phonon modes coupled most strongly with ionic conductivity, and assessing the role of anharmonicity, could therefore pave the way for discovering and designing new SEs via phonon engineering. In this work, we investigate phonons in Na 3 PS 4 and their coupling to fast Na diffusion, using a combination of neutron scattering, ab initio molecular dynamics (AIMD), and extended molecular dynamics based on machine-learned potentials. We identify that anharmonic soft-modes at the Brillouin zone boundary of the anharmonically stabilized cubic phase constitute key phonon modes that control the Na diffusion process in Na 3 PS 4 . We demonstrate how these strongly anharmonic phonon modes enable Na-ions to hop along the minimum energy pathways. Further, the quasi-elastic neutron scattering (QENS) measurements, supplemented with ab initio and machine-learned molecular dynamics simulations, probe the Na diffusivity and diffusion mechanism. These results offer detailed microscopic insights into the dynamic mechanism of fast Na diffusion and provide an avenue to search for further Na solid electrolytes.

42 ENGINEERING↗
DOE OSTI2026

Diameter-dependent multiple proton jumps dictate hydronium and hydroxide transport in carbon nanotubes

Nanofluidic channels impose extreme confinement on water, giving rise to unusual transport phenomena of the liquid. However, how the transport of hydroxide and hydronium ions is influenced by such confinement is still not fully understood. This study employs machine learning-accelerated simulations, based on the SCAN density functional, to investigate proton transfer dynamics in CNTs of varying diameters (0.8 nm to 2.8 nm). The extreme confinement of water inside a 0.8 nm CNT not only enhances the probability of multiple consecutive proton jumps, but also reverses the relative diffusion coefficient of hydronium and hydroxide ions in bulk water. In CNTs with diameters larger than 0.8 nm, hydronium diffuses slightly faster than in bulk water, whereas hydroxide diffusion slows because of its localization near CNT walls, hindering multiple proton jumps. This work highlights the significant impact of nanoscale confinement on proton transfer dynamics, with implications for designing nanoscale systems with controlled proton transport.

Chemistry↗
DOE OSTI2022

Effect of laser preheat in magnetized liner inertial fusion at OMEGA

Integrated magnetized liner inertial fusion (MagLIF) experiments on the OMEGA laser system are modeled to investigate the effects of laser preheat on implosion performance. In both simulations and experiments, preheating the fuel with a laser enhances the neutron yield, with the maximum yield occurring at an optimal preheat laser energy. Increasing the preheat energy past the optimal value reduces the neutron yield. In simulations, the rate of the drop in neutron yield depends on the inclusion of the Nernst effect. The Nernst effect advects the magnetic field away from the center of the fuel region during the preheat stage of MagLIF on OMEGA and weakens magnetic-flux compression. If the Nernst effect is not included, the simulated drop in yield past the optimal preheat laser energy is more gradual as opposed to the sharper drop seen in experiments. Simulations are able to model trend in measured ion temperatures seen in experiments. Here, a mix model is used to show that including mix from the wall in the simulated fuel region further degrades the yield and lowers the optimal preheat laser energy. Simulations with mix predict that increasing the initial axial magnetic field may still enhance yield performance in integrated implosions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Chemical evolution in nitrogen shocked beyond the molecular stability limit

Evolution of nitrogen under shock compression up to 100 GPa is revisited via molecular dynamics simulations using a machine-learned interatomic potential. The model is shown to be capable of recovering the structure, dynamics, speciation, and kinetics in hot compressed liquid nitrogen predicted by first-principles molecular dynamics, as well as the measured principal shock Hugoniot and double shock experimental data, albeit without shock cooling. Our results indicate that a purely molecular dissociation description of nitrogen chemistry under shock compression provides an incomplete picture and that short oligomers form in non-negligible quantities. Finally, this suggests that classical models representing the shock dissociation of nitrogen as a transition to an atomic fluid need to be revised to include reversible polymerization effects.

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

Deep learning-based predictive models for laser direct drive at the Omega Laser Facility

The rich and complex physics of inertial confinement fusion provides a unique and challenging space for high-fidelity first-principles modeling. Consequently, simulation codes that are used to design experiments are computationally expensive and lack the predictive capability required for extensive parameter exploration in search of a high-performing design for laser direct drive. In this article, we present two deep-learning-based predictive models intended to address these difficulties. The first model (TL DNN) acts as a fast emulator of simulations as well as experiments at the Omega Laser Facility. This model is trained on a simulation database and subsequently calibrated on experimental data using transfer learning. To facilitate the development of this model, an autoencoder is developed to reduce the dimensionality of the input space by compressing the laser pulse input. The model predicts key experimental scalar observables of Omega experiments with high accuracy and minimal computational cost. This deep neural net enables rapid exploration of a high-dimensional input parameter space for an optimal implosion design. The second model (DNN SM+) aims to extend the statistical modeling work of Lees et al. [Phys. Rev. Lett. 127, 105001 (2021)], by increasing the complexity of the model space and allowing for coupling between degradation terms. Since the model capacity of DNN SM+ is higher than the model of Lees et al., DNN SM+ can potentially provide an improvement in predictive capability, and we use this model to provide insight into complicated degradation dependencies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

An overview of magneto-inertial fusion on the Z Machine at Sandia National Laboratories

We present an overview of the magneto-inertial fusion (MIF) concept MagLIF (Magnetized Liner Inertial Fusion) pursued at Sandia National Laboratories and review some of the most prominent results since the initial experiments in 2013. In MagLIF, a centimeter-scale beryllium tube or "liner" is filled with a fusion fuel, axially pre-magnetized, laser pre-heated, and finally imploded using up to 20 MA from the Z machine. All of these elements are necessary to generate a thermonuclear plasma: laser preheating raises the initial temperature of the fuel, the electrical current implodes the liner and quasi-adiabatically compresses the fuel via the Lorentz force, and the axial magnetic field limits thermal conduction from the hot plasma to the cold liner walls during the implosion. MagLIF is the first MIF concept to demonstrate fusion relevant temperatures, significant fusion production (>10^13 primary DD neutron yield), and magnetic trapping of charged fusion particles. On a 60 MA next-generation pulsed-power machine, two-dimensional simulations suggest that MagLIF has the potential to generate multi-MJ yields with significant self-heating, a long-term goal of the US Stockpile Stewardship Program. At currents exceeding 65 MA, the high gains required for fusion energy could be achievable.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Molecular-Level Exploration of the Structure-Function Relations Underlying Interfacial Charge Transfer in the Subphthalocyanine/C 60 Organic Photovoltaic System

The arrangement of organic molecules at the donor-acceptor interface in an organic photovoltaic (OPV) cell can have a strong effect on the generation of charge carriers and thereby cell performance. In this paper, we report the molecular-level exploration of the ensemble of interfacial donor-acceptor pair geometries and the charge-transfer (CT) rates to which they give rise. Our approach combines molecular-dynamics simulations, electronic structure calculations, machine learning, and rate theory. This approach is applied to the boron subphthalocyanine chloride (donor) and C 60 (acceptor) OPV system. In this work we find that the interface is dominated by a previously unreported donor-acceptor pair edge geometry, which contributes significantly to device performance in a manner that depends on the initial conditions. Quantitative relations between the morphology and CT rates are established, which can be used to advance the design of more efficient OPV devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Event generation with normalizing flows

We present a novel integrator based on normalizing flows which can be used to improve the unweighting efficiency of Monte-Carlo event generators for collider physics simulations. In contrast to machine learning approaches based on surrogate models, our method generates the correct result even if the underlying neural networks are not optimally trained. We exemplify the new strategy using the example of Drell-Yan type processes at the LHC, both at leading and partially at next-to-leading order QCD.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Galaxy deblending using residual dense neural networks

We present a new neural network approach for deblending galaxy images in astronomical data using Residual Dense Neural network (RDN) architecture. We train the network on synthetic galaxy images similar to the typical arrangements of field galaxies with a finite point spread function (PSF) and realistic noise levels. The main novelty of our approach is the usage of two distinct neural networks: i) a deblending network which isolates a single galaxy postage stamp from the composite and, ii) a classifier network which counts the remaining number of galaxies. The deblending proceeds by iteratively peeling one galaxy at a time from the composite until the image contains no further objects as determined by the classifier, or by other stopping criteria. By looking at the consistency in the outputs of the two networks, we can assess the quality of the deblending. Further, we characterize the flux and shape reconstructions in different quality bins and compare our deblender with the industry standard, SExtractor. We also discuss possible future extensions for the project with variable PSFs and noise levels.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

SMEFT probes in future precision DIS experiments

We analyze the potential of future high-energy deep-inelastic scattering (DIS) experiments to probe new physics within the framework of the Standard Model effective field theory (SMEFT). We perform a detailed study of SMEFT probes at a future Large Hadron-electron Collider (LHeC) and a Future Circular lepton-hadron Collider (FCC-eh) machine, and extend previous simulations of the potential of an electron-ion collider (EIC) to include 𝑍-boson vertex corrections. Precision 𝑍-pole constraints on vertex corrections suffer from numerous degeneracies in the Wilson-coefficient parameter space. We find that both the LHeC and the FCC-eh can help remove these degeneracies present in the existing global fits of precision 𝑍-pole observables and LHC data. The FCC-eh and LHeC will in many cases improve upon the existing precision electroweak bounds on the SMEFT parameter space. This highlights the important role of precision DIS measurements for new physics studies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Unexpected Observation of Disorder and Multiple Phase-Transition Pathways in Shock-Compressed Zr

The response of materials under dynamic compression involves a complex interplay of various deformation mechanisms aimed at relieving shear stresses, yielding a remarkable diversity in material behavior. Here, in this Letter, we utilize femtosecond x-ray diffraction coupled with nanosecond laser compression to reveal an intricate competition between multiple shear-relieving mechanisms within an elemental metal. Our observations in shocked-compressed single-crystal Zr indicate a disorder-mediated shear relaxation at lower pressures. Above the phase-transition pressure, we observe the increasing contribution of structural phase transition in relieving shear stress. We detect not one but three concurrent pathways during the transition from the hcp to a hex-3 structure. These complex dynamics are partially corroborated through multimillion-atom molecular dynamics simulations employing a machine-learned interatomic potential. Our observation of multiple concurrent pathways and disorder during shock compression underscore the far greater intricacies in the dynamic response of metals than previously assumed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Scaling Superconducting Quantum Computers with Chiplet Architectures

Fixed-frequency transmon quantum computers (QCs) have advanced in coherence times, addressability, and gate fidelities. Unfortunately, these devices are restricted by the number of on-chip qubits, capping processing power and slowing progress toward fault-tolerance. Although emerging transmon devices feature over 100 qubits, building QCs large enough for meaningful demonstrations of quantum advantage requires overcoming many design challenges. For example, today’s transmon qubits suffer from significant variation due to limited precision in fabrication. As a result, barring significant improvements in current fabrication techniques, scaling QCs by building ever larger individual chips with more qubits is hampered by device variation. Severe device variation that degrades QC performance is referred to as a defect. Here, we focus on a specific defect known as a frequency collision. When transmon frequencies collide, their difference falls within a range that limits two-qubit gate fidelity. Frequency collisions occur with greater probability on larger QCs, causing collision-free yields to decline as the number of on-chip qubits increases. As a solution, we propose exploiting the higher yields associated with smaller QCs by integrating quantum chiplets within quantum multi-chip modules (MCMs). Yield, gate performance, and application-based analysis show the feasibility of QC scaling through modularity. Our results demonstrate that chiplet architectures, relative to monolithic designs, benefit from average yield improvements ranging from 9.6 – 92.6 × for ≲5 qubit machines. In addition, our simulations explore the design space of chiplet systems and discover configurations that demonstrate average two-qubit gate infidelity reductions that are at best 0.815 × their monolithic counterpart. Lastly, we observe that carefully-selected modular systems achieve fidelity improvements on a range of benchmark circuits.

quantum architecture↗