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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 559 records · Page 31

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DOE OSTI2025

Structure-dependent clustering-to-declustering solute segregation transitions near disconnections

Grain-boundary disconnections, characterized by a step and a dislocation, are pervasive interfacial line defects that play a critical role in governing the properties and performance of nanocrystalline alloys. Although segregation of alloying elements is frequently observed at GB disconnections, the underlying mechanisms remain poorly understood, particularly at elevated temperatures and non-dilute conditions. In this study, we employ atomistic simulations to study the segregation behavior of Ag at various faulted disconnections in Cu as a model material system. Our results demonstrate a pronounced size and compactness effect on the segregation behavior: more compact faulted disconnection structures promote the formation of Ag segregation clusters due to a highly localized tensile field, whereas more spread faulted disconnection structures (i.e., with wider partial dislocation spacing) exhibit much weaker clustering tendencies. Furthermore, with increasing temperature, Ag clustering in small disconnections initially intensifies and then disappears, indicating a thermally driven transition from clustering to declustering segregation behavior.

Disconnections↗

DOE OSTI2024

Effect of Solvent Composition on Non-DLVO Forces and Oriented Attachment of Zinc Oxide Nanoparticles

Oriented attachment (OA) occurs when nanoparticles in solution align their crystallographic axes prior to colliding and subsequently fuse into single crystals. Traditional colloidal theories such as DLVO provide a framework for evaluating OA but fail to capture key particle interactions due to the atomistic details of both the crystal structure and the interfacial solution structure. Using zinc oxide as a model system, we investigated the effect of the solvent on short-ranged and long-ranged particle interactions and the resulting OA mechanism. In situ TEM imaging showed that ZnO nanocrystals in toluene undergo long-range attraction comparable to 1kT at separations of 10 nm and 3kT near particle contact. These observations were rationalized by considering non-DLVO interactions, namely dipole-dipole forces and torques between the polar ZnO nanocrystals. Langevin dynamics simulations showed stronger interactions in toluene compared to methanol solvents, consistent with the experimental results. Concurrently, we performed atomic force microscopy measurements using ZnO-coated probes for the short-ranged interaction. Our data provided valuable insights into another type of non-DLVO interaction, namely the repulsive solvation force. Specifically, the solvation force was stronger in water compared to ethanol and methanol, due to the stronger hydrogen bonding and denser packing of water molecules at the interface. In conclusion, our results highlight the importance of non-DLVO forces in a general framework for understanding and predicting particle aggregation and attachment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Topological energy barrier for skyrmion lattice formation in MnSi

We report the direct measurement of the topological skyrmion energy barrier through a hysteresis of the skyrmion lattice in the chiral magnet MnSi. Measurements were made using small-angle neutron scattering with a custom-built resistive coil to allow for high-precision minor hysteresis loops. The experimental data were analyzed using an adapted Preisach model to quantify the energy barrier for skyrmion formation and corroborated by the minimum-energy path analysis based on atomistic spin simulations. Here, we reveal that the skyrmion lattice in MnSi forms from the conical phase progressively in small domains, each of which consisting of hundreds of skyrmions, and with an activation barrier of several eV.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

A Modeling and Neutron Diffraction Study of the High Temperature Properties of Sub-Stoichiometric Yttrium Hydride for Novel Moderator Applications

Low-enriched-uranium (LEU) reactor systems utilize moderators to improve neutron economy. Solid yttrium hydride is one of the primary moderator candidates for high-temperature (>700 °C) nuclear reactor applications. This is due to its ability to retain hydrogen at elevated temperatures compared to other metal hydrides. For reactor modeling purposes, both neutronic and thermos-mechanical modeling, several high-temperature properties for sub-stoichiometric yttrium hydride (YH2−x) are needed. In this paper, we present an atomistics and a neutron diffraction study of the high-temperature properties of Y and YH2−x. Specifically, we focus on the thermal lattice expansion effects in yttrium metal and yttrium hydride, which also govern bulk thermal expansion. Previously reported physical and mechanical properties for sub-stoichiometric yttrium hydride at ambient conditions are expanded using lattice dynamics to take into account high-temperature effects. Accordingly, an array of newly generated properties is presented that enables high-fidelity neutronics, and thermomechanical modeling. These properties include various elastic moduli, thermal expansion parameters for yttrium and yttrium hydride, and single-phase (YH2−x) and two-phase (Y + YH2−x) density as a function of stoichiometry and density.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Quantum bath augmented stochastic nonequilibrium atomistic simulations for molecular heat conduction

Classical molecular dynamics (MD) has been shown to be effective in simulating heat conduction in certain molecular junctions since it inherently takes into account some essential methodological components which are lacking in the quantum Landauer-type transport model, such as many-body full force-field interactions, anharmonicity effects and nonlinear responses for large temperature biases. However, the classical MD reaches its limit in the environments where the quantum effects are significant (e.g. with low-temperatures substrates, presence of extremely high frequency molecular modes). Here, we present an atomistic simulation methodology for molecular heat conduction that incorporates the quantum Bose–Einstein statistics into an “effective temperature” in the form of a modified Langevin equation. We show that the results from such a quasi-classical effective temperature MD method deviates drastically when the baths temperature approaches zero from classical MD simulations and the results converge to the classical ones when the bath approaches the high-temperature limit, which makes the method suitable for full temperature range. In addition, we show that our quasi-classical thermal transport method can be used to model the conducting substrate layout and molecular composition (e.g. anharmonicities, high-frequency modes). Anharmonic models are explicitly simulated via the Morse potential and compared to pure harmonic interactions to show the effects of anharmonicities under quantum colored bath setups. Finally, the chain length dependence of heat conduction is examined for one-dimensional polymer chains placed in between quantum augmented baths.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

A density-functional theory study of the Al/AlOx/Al tunnel junction

The aluminum oxide tunnel junction is a key component of the majority of superconducting quantum devices. For high-quality, reproducible, and scalably manufacturable qubits, the ability to fabricate Josephson junctions (JJs) with a targeted critical current and high uniformity is essential. In this study, we use first-principles modeling to assess fundamental aspects of the atomic structure of both amorphous and crystalline aluminum oxide tunnel junctions and relate the structure to predicted performance metrics. We use modified ab initio molecular dynamics to develop realistic models of the tunnel junction, from which interface roughness and local thickness fluctuations are analyzed in an unbiased manner by training a neural network to identify the boundary between metal and oxide. We show that the effective thickness of the insulating part of the junction can be different from the apparent physical thickness. We calculate the rate of Cooper pair tunneling for the atomically resolved electrostatic potential using direct numerical solution in 3D, which shows a channeling effect that impacts the junction critical current. The predicted critical current is a useful JJ design parameter that can be accessed from the ab initio calculations without fitting parameters. To assess the limits of uniformity and fabrication choices (e.g., oxidation vs epitaxy), we compare the amorphous junctions to crystalline models, which show order of magnitude more efficient tunneling compared to the amorphous case, underlining the connection between atomistic structure and Cooper pair tunneling efficiency. Further, this work provides a foundation for ab initio materials design and evaluation to help accelerate future development of improved tunnel junctions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Nuclear–Electronic Orbital QM/MM Approach: Geometry Optimizations and Molecular Dynamics

Hybrid quantum mechanical/molecular mechanical (QM/MM) methods allow simulations of chemical reactions in atomistic solvent and heterogeneous environments such as proteins. Herein, the nuclear–electronic orbital (NEO) QM/MM approach is introduced to enable the quantization of specified nuclei, typically protons, in the QM region using a method such as NEO-density functional theory (NEO-DFT). This approach includes proton delocalization, polarization, anharmonicity, and zero-point energy in geometry optimizations and dynamics. Expressions for the energies and analytical gradients associated with the NEO-QM/MM method, as well as the previously developed polarizable continuum model (NEO-PCM), are provided. Geometry optimizations of small organic molecules hydrogen bonded to water in either dielectric continuum solvent or explicit atomistic solvent illustrate that aqueous solvation can strengthen hydrogen-bonding interactions for the systems studied, as indicated by shorter intermolecular distances at the hydrogen-bond interface. We then performed a real-time direct dynamics simulation of a phenol molecule in explicit water using the NEO-QM/MM method. Furthermore, these developments and initial examples provide the foundation for future studies of nuclear–electronic quantum dynamics in complex chemical and biological environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Machine learning in materials science: From explainable predictions to autonomous design

The advent of big data and algorithmic developments in the field of machine learning (and artificial intelligence, in general) have greatly impacted the entire spectrum of physical sciences, including materials science. Materials data, measured or computed, combined with various techniques of machine learning have been employed to address a myriad of challenging problems, such as, development of efficient and predictive surrogate models for a range of materials properties, screening and down-selection of novel candidate materials for targeted applications, new methodologies to improve and further expedite molecular and atomistic simulations, with likely many more important developments to come in the foreseeable future. While the applications thus far have provided a glimpse of the true potential data-enabled routes have to offer, it has also become clear that further progress in this direction hinges on our ability to understand, explain and rationalize findings of a machine learning model in light of the domain-knowledge. This focused review provides an overview of the main areas where machine learning has been widely and successfully used in materials science. Subsequently, a brief discussion of several techniques that have been helpful in extracting physically-meaningful insights, causal relationships and design-centric knowledge from materials data is provided. Finally, we identify some of the imminent opportunities and challenges that materials community faces in this exciting and rapidly growing field.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers

Excitement at the prospect of using data-driven generative models to sample configurational ensembles of biomolecular systems stems from the extraordinary success of these models on a diverse set of high-dimensional sampling tasks. Unlike image generation or even the closely related problem of protein structure prediction, there are currently no data sources with sufficient breadth to parametrize generative models for conformational ensembles. To enable discovery, a fundamentally different approach to building generative models is required: models should be able to propose rare, albeit physical, conformations that may not arise in even the largest data sets. Here, in this work, we introduce a modular strategy to generate conformations based on “backmapping” from a fixed protein backbone that (1) maintains conformational diversity of the side chains and (2) couples the side-chain fluctuations using global information about the protein conformation. Our model combines simple statistical models of side-chain conformations based on rotamer libraries with the now ubiquitous transformer architecture to sample with atomistic accuracy. Together, these ingredients provide a strategy for rapid data acquisition and hence a crucial ingredient for scalable physical simulation with generative neural networks.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Grazing Incidence Wide-Angle X-ray Scattering of Water Adsorption in Polyamide Barrier Layers of Reverse Osmosis Membranes

To understand the relationship between the intermolecular structure of aromatic polyamide (PA) scaffold and the water molecules in the barrier layers of reverse osmosis (RO) membranes, a grazing incidence wide-angle X-ray scattering (GIWAXS) study was carried out on freestanding PA thin films at varying relative humidity (RH) conditions. The scattering results were analyzed by an interference scattering model, containing a phase factor between a PA chain and an adsorbed water molecule. This model yielded good fits to the GIWAXS profiles where the water adsorption was found to vary linearly with RH. Atomistic molecular dynamics (MD) simulations were also performed to complement the experimental study. Furthermore, the simulations revealed that a rapid condensation layer initially formed on the PA film surface, followed by the slow water molecule diffusion inside the PA membrane. Sparse adsorbed water, isolated in subnanopores of the PA film adjacent to the polar atoms, even in very low quantities, modifies the X-ray scattering. Atomistic simulations at the microscopic scale provide partial support for several X-ray scattering findings.

36 MATERIALS SCIENCE↗

DOE OSTI2023

An interplay between a hydrogen atmosphere and dislocation characteristics in BCC Fe from time-averaged molecular dynamics

The interplay between hydrogen and dislocations (e.g., core and elastic energies, and dislocation–dislocation interactions) has implications on hydrogen embrittlement but is poorly understood. Continuum models of hydrogen enhanced local plasticity have not considered the effect of hydrogen on dislocation core energies. Energy minimization atomistic simulations can only resolve dislocation core energies in hydrogen-free systems because hydrogen motion is omitted so hydrogen atmosphere formation can’t occur. Additionally, previous studies focused more on face-centered-cubic than body-centered-cubic metals. Discrete dislocation dynamics studies of hydrogen–dislocation interactions assume isotropic elasticity, but the validity of this assumption isn’t understood. Here, we perform time-averaged molecular dynamics simulations to study the effect of hydrogen on dislocation energies in body-centered-cubic iron for several dislocation character angles. We see atmosphere formation and highly converged dislocation energies. We find that hydrogen reduces dislocation core energies but can increase or decrease elastic energies of isolated dislocations and dislocation–dislocation interaction energies depending on character angle. We also find that isotropic elasticity can be well fitted to dislocation energies obtained from simulations if the isotropic elastic constants are not constrained to their anisotropic counterparts. These results are relevant to ongoing efforts in understanding hydrogen embrittlement and provide a foundation for future work in this field.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

1D EDIPIC

EDIPIC codes were developed for simulation of kinetic effects in low-temperature plasmas. They can be used for simulation of plasmas in electrostatic approximation and include many atomistic and plasma-surface interaction processes needed for simulations of low temperature plasmas. These codes include a detailed model of secondary electron emission induced by electrons and ions. The Monte-Carlo model of electron-neutral collisions and the Langevin model of Coulomb collisions for electrons are also implemented.

Sydorenko, Dmytro↗

DOE OSTI2021

EDIPIC-2D

Sydorenko, Dmytro↗

DOE OSTI2022

Phase-field simulations to inform nuclear fuel performance modeling

Software tools to simulate nuclear fuel performance at the engineering scale, such as Idaho National Laboratory (INL)’s BISON code, are increasingly relied upon in regulatory and economic decision-making. However, accurate results from these tools depends on the availability of materials parameters that are used as input. In recent years, atomistic and mesoscale simulation methods have emerged as a cost-effective, expedient means to obtain such input parameters. Phase-field simulations using INL’s Marmot application have been used to obtain microstructure-level parameters and to improve material models for fuel performance modeling using BISON. In this talk, recent examples of this process are given, including applications in UO2, U3Si2, and UZr fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Crystallographic and temperature effects in low-energy collisions for plasma–material interactions

The interaction of plasma with materials is critical for the fundamental understanding of non-equilibrium processes and their wide application. Recent achievements in fusion energy research emphasize the importance of this problem. Because modelling and predicting plasma–material interactions (PMI) require considering a tremendous number of single plasma particle–surface interaction events, this type of modelling was, until recently, possible only within a binary collision approximation (BCA). The BCA approach considers materials as uniform temperature-insensitive media. The research presented here addresses the PMI problem within an atomistic-based approach for the first time at a statistically significant level. Approximately 10 5 molecular dynamics trajectories were generated for 100 eV deuterium ions interacting with a tungsten surface. The research demonstrated the critical importance of incorporating the discrete lattice structure of matter into the model. Deuterium penetration depth and fraction of backscattered deuterium ions strongly depend on the surface orientation, the impact incident ion directions, and the ions’ initial positions. On average, the BCA-based calculations underestimate the penetration depth by a factor of two or more, and the fraction of backscattered atoms is overestimated by a factor of two or more. As a result, increasing the material temperature from 500 to 3000 K reduces penetration depth by about 30% and increases the fraction of backscattered atoms by 250%.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Morphological descriptors of nanoparticles: The link between atomistic structures and x-ray absorption spectra

Understanding and quantifying the morphology of nanoparticles are essential for linking their atomic structure to diverse applications and verifying theoretical models. While experimental information on the structure of nanoparticles in the size range below ∼5 nm can be extracted from x-ray absorption spectroscopy using a small number of descriptors—most commonly coordination numbers—developing an understanding of morphology descriptors from experimental data remains a challenge. Here, in this study, we introduce NanoGene, a genetic algorithm-based method for generating structurally diverse nanoparticle models guided by user-defined descriptors. We establish correlations among structural, size-related, and morphological descriptors and demonstrate how experimentally accessible parameters, such as coordination numbers, can be leveraged to infer otherwise inaccessible ones, such as the generalized coordination number or particle oblateness. Principal component and clustering analyses reveal the relative importance of descriptors, with the number of atoms emerging as a key discriminant of the nanoparticle structure. By providing both the methodology and an extensive dataset of nanoparticle geometries, this work offers a practical foundation for descriptor-based analysis and interpretation of experimental observations, bridging the gap between local atomic coordinates and global morphological characterization.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2025

A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processes

Plasma-surface interactions (PSI) play a crucial role in microelectronics fabrication; however, their multiscale nature and array of complex, often unknown interactions make computational modeling of PSIs extremely difficult. To this end, we propose a general neural master equation (NME) framework that uses master equations to describe the dynamics of a molecular process, wherein neural networks learned from atomistic simulations represent unknown transitions between different system states. By leveraging the physics-based structure of master equations and data-driven state transitions, the NME framework promotes generalizability and physics interpretability, and can bridge disparate length and time scales. The framework is demonstrated for multiscale modeling of Si atomic layer etching and reactive ion etching, where the learned NME-based surface kinetic models exhibit good predictive and extrapolative capabilities for predicting experimentally relevant observables as a function of process parameters. The NME-based surface kinetic models obey physical constraints, which are violated in models based on neural ordinary differential equations. The proposed NME framework for multiscale modeling of molecular processes can pave the way for the discovery of new chemistries and materials in atomic-scale plasma processes.

Chemical engineering↗

DOE OSTI2023

Integrated Multiscale Model for Design of Robust 3D Solid-state Lithium Batteries

In FY23, we successfully established the multiscale modeling framework for probing the effects of materials microstructure on cell performance of 3D solid-state batteries. The framework covers physicochemical processes co-evolving at the atomistic and microstructure scales. Our simulations revealed the mechanism of initial interfacial degradation, formation of secondary phases, and the structure-property relationship for ion transport and mechanical stability at the interface. In addition, we also established the microstructure-performance relationships by performing sensitivity tests of various microstructure features and extracting their impact on cell performance during charge-discharge cycles. We have successfully applied our multiscale, multiphysics modeling capability to common electrode and electrolyte materials that are of interests to VTO and the experimental teams within the US-Germany collaboration. The insights we obtained from these simulations provide valuable design principles to optimize materials properties for advanced 3D solid-state batteries.

25 ENERGY STORAGE↗