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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Solid-state Precipitation in Molecular Dynamics: KMC-MD Hybrid Simulations

A critical drawback of molecular dynamics (MD) simulations when modeling physical properties is the limitation to short timescales. Solid state precipitation is one behavior that is not significantly observable at timescales accessible to MD, and thus has been difficult to simulate at an MD scale. Taking inspiration from Kinetic Monte-Carlo (KMC) methods for simulating diffusion on a rigid lattice, we have developed a hybrid KMC-MD approach to enable simulation of solid-state precipitation during MD simulation implemented in LAMMPS. This method incorporates the atomic neighbor hopping behavior utilized by KMC simulations into a MD simulation with a variable local lattice configuration. By accelerating nearest-neighbor atomic hopping with this technique, solid-state precipitation can be observed at an MD timescale. Utilizing theory based on persistent-embryo methods for liquid-solid precipitation in MD we demonstrate the calculation of critical nucleus size for the gamma’ precipitation out of gamma phase in the Ni-Al system.

Molecular Dynamics↗
NASA NTRSDate not supplied

Solid-state Precipitation in Molecular Dynamics: kMC-MD Hybrid Simulations

A critical drawback of molecular dynamics (MD) simulations when modeling physical properties is the limitation to short timescales. Solid state precipitation is one behavior that is not significantly observable at timescales accessible to MD, and thus has been difficult to simulate at an MD scale. Taking inspiration from Kinetic Monte-Carlo (KMC) methods for simulating diffusion on a rigid lattice, we have developed a hybrid KMC-MD approach to enable simulation of solid-state precipitation during MD simulation implemented in LAMMPS. This method incorporates the atomic neighbor hopping behavior utilized by KMC simulations into a MD simulation with a variable local lattice configuration. By accelerating nearest-neighbor atomic hopping with this technique, solid-state precipitation can be observed at an MD timescale. Utilizing theory based on persistent-embryo methods for liquid-solid precipitation in MD we demonstrate the calculation of critical nucleus size for the gamma’ precipitation out of gamma phase in the Ni-Al system.

Molecular Dynamics↗
NASA NTRS2020

Ultrafast Melting at Metal-Semiconductor Interfaces: A Molecular Dynamics Study

Quantifying the thermodynamics of interfacial melting is important for several applications such as laser machining and designing radiation hardened electronic devices. Due to the short time and length scales that are involved in the process, molecular simulations provide an ideal path for understating the interfacial melting phenomena. In this work we investigate tungsten (W) – silicon carbide (SiC) interfaces which are relevant in modern high-power electronic devices. Classical molecular dynamics simulations along with the two-temperature model (TTM) were used to study the radiation induced melting of the interface while a neural network-based machine learning framework was used to track the transient re-crystallization process. The damage endured by the semiconductor at the interface following the radiation event is discussed in detail while the re-crystallization of the metal is described in terms of the classical nucleation theory.

Ashwin Ravichandran↗
NASA NTRSDate not supplied

Predicting Melt Properties Using Atomistic Simulations With A Highly Accurate Physically Informed Neural Network Interatomic Potential

The use of a recently developed machine learning (ML) interatomic potential for molecular dynamics simulations of aluminum melt properties will be presented. Such properties are critical for process modeling in additive manufacturing, including the melt pool size, solidification, and formation of solidification microstructures. Direct first-principles modeling of these processes is computationally prohibitive whereas simulations employing ML potentials combine the high accuracy of quantum-mechanical methods with high computational speeds. The physically-informed neural network (PINN) method used herein, integrates a high-dimensional regression implemented by an artificial neural network with a physics-based bond-order interatomic potential. PINN potentials can accurately reproduce many properties of aluminum in both crystalline-solid and liquid phases. We examine the accuracy of a PINN Al potential in predicting the density, self-diffusivity, viscosity, and the tension of the liquid surface and liquid-solid interfaces. Comparison with experimental data and ab initio molecular dynamics calculations shows very good agreement for all properties tested.

molecular dynamics↗
DOE OSTI2025

Neuromorphic heat transport effects in a molecular junction

Understanding energy transport at the nanoscale is an open and fundamental challenge in the molecular sciences with direct implications for the design of new electronics, computing devices, and materials. While nanoscale energy transport under steady-state conditions has been studied extensively, there is much less known about energy transport under time-dependent driving forces, particularly in the far-from-equilibrium regime. In this work, we use nonequilibrium molecular dynamics simulations and stochastic thermodynamics to investigate energy transport in a well-studied nanoscale system—a molecular junction—subjected to a time-periodic temperature gradient. The primary observation is that molecular junctions can exhibit heat transport hysteresis, a phenomenon in which the heat flux through a system depends not only on the instantaneous value of a time-dependent temperature bias but also on the temporal history of that bias. The presented findings illustrate that molecular junctions can exhibit the specific memory effect—heat transport hysteresis—that is essential for the design of thermal neuromorphic computers. This work elucidates a potential pathway toward the realization of such devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Developing the Science Basis for Understanding Polymer Encapsulant Degradation Mechanisms: DuraMAT 2.0 Final Project Report

Polymeric encapsulants are essential materials in photovoltaic modules, protecting sensitive electronics from the environment while providing mechanical integrity to the multilayered assembly. However, these polymeric materials are susceptible to degradation processes driven by the ingress of environmental species, ultraviolet radiation, thermal stresses, and mechanical loading. In this study, we employ a combined atomistic simulation and accelerated aging experimental approach to study the molecular-scale mechanisms of encapsulant degradation. Classical molecular dynamics simulations quantify the diffusion of environmental and degradation species through the polymer matrix, producing composition-specific diffusion coefficients. Reactive simulations characterize activation energy barriers and reaction rate constants for key chemical pathways. In parallel, thermal-desorption analyses coupled with mass spectrometry monitor the emergence and concentration profiles of degradation products under controlled stressor conditions. By integrating simulation and experiment, we establish quantitative correlations between polymer composition, species diffusivity, and chemical reactivity. We anticipate that these relations and quantitative values could serve as high-fidelity inputs to reaction-diffusion models, enabling physics-informed lifetime predictions and guiding the design of more durable encapsulant materials for solar energy applications.

36 MATERIALS SCIENCE↗
NASA NTRS2014

Modeling Materials: Design for Planetary Entry, Electric Aircraft, and Beyond

NASA missions push the limits of what is possible. The development of high-performance materials must keep pace with the agency's demanding, cutting-edge applications. Researchers at NASA's Ames Research Center are performing multiscale computational modeling to accelerate development times and further the design of next-generation aerospace materials. Multiscale modeling combines several computationally intensive techniques ranging from the atomic level to the macroscale, passing output from one level as input to the next level. These methods are applicable to a wide variety of materials systems. For example: (a) Ultra-high-temperature ceramics for hypersonic aircraft-we utilized the full range of multiscale modeling to characterize thermal protection materials for faster, safer air- and spacecraft, (b) Planetary entry heat shields for space vehicles-we computed thermal and mechanical properties of ablative composites by combining several methods, from atomistic simulations to macroscale computations, (c) Advanced batteries for electric aircraft-we performed large-scale molecular dynamics simulations of advanced electrolytes for ultra-high-energy capacity batteries to enable long-distance electric aircraft service; and (d) Shape-memory alloys for high-efficiency aircraft-we used high-fidelity electronic structure calculations to determine phase diagrams in shape-memory transformations. Advances in high-performance computing have been critical to the development of multiscale materials modeling. We used nearly one million processor hours on NASA's Pleiades supercomputer to characterize electrolytes with a fidelity that would be otherwise impossible. For this and other projects, Pleiades enables us to push the physics and accuracy of our calculations to new levels.

Supercomputing↗
DOE OSTI2024

Simulating Crystallization in a Colloidal System Using State Predictive Information Bottleneck Based Enhanced Sampling

Here, we investigate crystal nucleation in supersaturated colloid suspensions using enhanced molecular dynamics simulations augmented with machine learning techniques. The simulations reveal that crystallization in the model colloidal system studied here, with particles interacting through a repulsive screened Coulomb Yukawa potential, proceeds from vapor to dense liquid droplet to crystalline phases across multiple high barriers. Employing a one-dimensional reaction coordinate derived from the State Predictive Information Bottleneck framework, our simulations capture back-and-forth phase transitions across multiple barriers effectively in biased metadynamics simulations. We obtain relative free energy differences between different phases and also quantify the roles of different molecular level features in driving the phase changes.

Chemistry↗
DOE OSTI2025

Integrating Experiments and Simulations to Reveal Anisotropic Growth Mechanisms and Interfaces of a One-Dimensional Zeolite

Zeolites are nanoporous crystalline materials critical for diverse industrial applications, yet their growth mechanisms are poorly understood. Here, this study presents a novel integrated framework combining experimental synthesis, high-resolution imaging, coarse-grained molecular dynamics simulations, and computer vision to uncover the mechanisms of growth of SSZ-24, a 1D channel zeolite. We demonstrate how synthesis conditions, such as temperature and reactant concentration, govern crystal anisotropy and surface roughness with growth dynamics differing markedly by crystallographic orientation. Along the channels, growth involves minimal energy barriers and rapid nucleation, resulting in rough surfaces. In contrast, growth perpendicular to the channels requires cooperative molecular organization and is highly sensitive to thermodynamic and kinetic conditions, yielding smooth anisotropic surfaces under low driving forces. By simulating transmission electron microscopy (TEM) images, we bridge molecular-scale simulations with experimental observations, identifying distinct growth mechanisms along different crystal planes. This work offers molecular-level insights into zeolite crystallization, advancing the rational design of nanoporous materials. The integration of cross-disciplinary methodologies establishes a transformative framework for optimizing zeolite synthesis, with implications for broader classes of materials.

Bertolazzo, Andressa A. [Univ. of Utah, Salt Lake ↗
DOE OSTI2025

Molecular-resolution imaging of ice crystallized from liquid water by cryogenic liquid-cell TEM

Despite the ubiquity of ice, a molecular-resolution image of nanoscopic defects or microstructures in ice crystallized from liquid water has never been obtained. This is mainly due to the difficulties in preparing and preserving crystalline ice samples that can survive under high-resolution imaging conditions. Here, we report the stabilization and Å-resolution electron imaging of ice Ih crystallized from liquid water by developing cryogenic liquid-cell transmission electron microscopy (CRYOLIC-TEM). We combine lattice mapping with molecular dynamics simulations to reveal that ice formation is highly tolerant to nanoscale defects such as misoriented subdomains and trapped gas bubbles, which are stabilized by molecular-scale structural motifs. Importantly, bubble surfaces adopt low-energy nanofacets and create negligible strain fields in the surrounding crystal. These bubbles can dynamically nucleate, grow, migrate, dissolve, and coalesce under electron irradiation and be monitored in situ near a steady state. This work improves our understanding of water crystallization behaviors at a molecular spatial resolution.

Materials science↗
DOE OSTI2025

Reactivity of Carbonate Solvent Electrolytes on Lithium Silicon Anodes

Silicon (Si) is promising for lithium-ion battery (LIB) anodes due to their high theoretical capacity and low electrochemical potential. However, significant challenges remain, including severe volumetric expansion during cycling and the electrochemical instability of electrolytes, which leads to the formation of a nonuniform solid electrolyte interphase (SEI). To investigate SEI formation mechanisms, computational molecular dynamics simulations offer valuable insights. In this work, we examine the trajectories and charge transfer behavior of lithium hexafluorophosphate (LiPF 6 ) salt with various solvent compositions using density functional theory (DFT) and ab initio molecular dynamics (AIMD). Among the tested electrolyte systems, LiPF 6 with vinylene carbonate (VC) added to ethyl methyl carbonate (EMC) exhibits the lowest reactivity with the Si anode. In contrast, the effects of fluoroethylene carbonate (FEC) and VC depend on whether the primary solvent is EMC alone or a mixture of ethylene carbonate (EC) and EMC. Moreover, we show that electrolyte reactivity varies with the degree of lithiation of the Si anode (LiSi vs Li 15 Si 4 ) and under different charge states. To decouple electrolyte reactivity from surface effects, we analyze the dissociation and formation energies of individual species from solvated configurations. Overall, these first-principles-based findings provide a strategic foundation for electrolyte design to improve cycling stability and extend calendar life in LIBs using Si anodes.

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

Structural and Dynamic Heterogeneity of Deep Eutectic Solvents Composed of Choline Chloride and Ortho-Phenol Derivatives

Structural, thermal, and dynamic properties of four deep eutectic solvents comprising choline chloride paired with ortho-phenolic derivative hydrogen-bond donors were probed using experiments and molecular simulations. The hydrogen-bond donors include phenol, catechol, o-chlorophenol, and o-cresol, in a 3:1 mixture with the hydrogen-bond acceptor choline chloride. Density, viscosity, and pulsed-field gradient NMR diffusivity measurements were conducted over a range of temperatures. Classical and ab initio molecular dynamics simulation results match experimental data reasonably well. Furthermore, the simulation results were then used to perform a more detailed analysis of the local structure and dynamics of these systems.

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

SynopFrame: Multiscale time-dependent visual abstraction framework for analyzing DNA nanotechnology simulations

We present an open-source framework, SynopFrame, that allows DNA nanotechnology (DNA-nano) experts to analyze and understand molecular dynamics simulation trajectories of their designs. We use a multiscale multi-dimensional abstraction space, connect the representations to a projected conformational space plot of the structure’s temporal sequence, and thus enable experts to analyze the dynamics of their structural designs and, specifically, failure cases of the assembly. In addition, our time-dependent abstraction representation allows the biologists, for the first time in a smooth and structurally clear way, to identify and observe temporal transitions of a DNA-nano design from one configuration to another, and to highlight important periods of the simulation for further analysis. We realize SynopFrame as a dashboard of the different synchronized 3D spatial and 2D schematic visual representations, with a color overlay to show essential properties such as the status of hydrogen bonds. The linking of the spatial, schematic, and abstract views ensures that users can effectively analyze the high-frequency motion. We also categorize the status of the hydrogen bonds into a new format to allow us to color-encode it and overlay it on the representations. To demonstrate the utility of SynopFrame, we describe example usage scenarios and report user feedback.

Abstraction space↗
DOE OSTI2026

Elastic strain engineering of lattice thermal conductivity of silicon: An ab-initio study

Silicon (Si) is the most essential material in the semiconductor industry. It is important to manage the thermal properties of crystalline Si. Elastic strain engineering (ESE) has proven to be an effective tool in controlling the electrical conductivity of Si in strained-silicon technology; its effects on the thermal conductivity of silicon, therefore, warrants careful investigation. The ESE effect is much more pronounced for nanostructured materials due to the ultralarge elastic strains (on the order of 10%) achievable at the nanoscale. In this work, the lattice thermal conductivity (κ L ) of Si under hydrostatic, biaxial, and uniaxial strain states is studied with ab-initio simulations, and the values of strain-dependent κ L compare well with experimental results and existing molecular dynamics simulations. To understand the mechanisms of strain-modulated κ L , the phonon bands, scattering rate, and Grüneisen parameters of phonon modes are computed. It is shown that strain can significantly change the anharmonicity of the crystal system, thus changing phonon scattering rates and κ L . Our results demonstrate that ESE can reduce silicon κ L by up to approximately 90%. Furthermore, uniaxial and biaxial strains can induce highly anisotropic thermal conductivity in Si, with relative variations up to 58.5% and 14.5%, respectively.

Anisotropy in Thermal Conductivity↗
DOE OSTI2024

Surface molecular pump enables ultrahigh catalyst activity

The performance of electrocatalysts is critical for renewable energy technologies. While the electrocatalytic activity can be modulated through structural and compositional engineering following the Sabatier principle, the insufficiently explored catalyst-electrolyte interface is promising to promote microkinetic processes such as physisorption and desorption. By combining experimental designs and molecular dynamics simulations with explicit solvent in high accuracy, we demonstrated that dimethylformamide can work as an effective surface molecular pump to facilitate the entrapment of oxygen and outflux of water. Dimethylformamide disrupts the interfacial network of hydrogen bonds, leading to enhanced activity of the oxygen reduction reaction by a factor of 2 to 3. This strategy works generally for platinum-alloy catalysts, and we introduce an optimal model PtCuNi catalyst with an unprecedented specific activity of 21.8 ± 2.1 mA/cm 2 at 0.9 V versus the reversible hydrogen electrode, nearly double the previous record, and an ultrahigh mass activity of 10.7 ± 1.1 A/mg Pt .

Science & Technology - Other Topics↗
DOE OSTI2024

Evidence of short chains in liquid sulfur

High energy x-ray pair distribution function measurements show the average coordination number of the first shell in liquid sulfur is 1.86 ± 0.04 across the λ-transition, not precisely 2.0 as widely accepted. This indicates that upon melting, liquid sulfur does not comprise solely of S 8 rings but also possesses a significant number of short chains. Intensities of the pre-peak and first diffraction peak of the x-ray structure factor and third peak height of the pair distribution function all show deviations at the λ-transition temperature T λ , associated with the break-up of S 8 rings and the start of oligomer polymerization. A significant number of non-bonded or loosely bonded “interstitial atoms,” with an average coordination number of 0.20 ± 0.005, are also observed in the so-called “forbidden zone” between the first and second shells upon melting. The number of interstitial atoms is found to decrease to a minimum at the λ-transition, but the majority persist into the high temperature polymerized liquid. Furthermore, the existence of short chains and nearby interstitial atoms represent the two main factors required to initiate the S 8 -ring to chain transition, as proposed by recent molecular dynamics simulations.

Chemical bonding↗