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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Ab-initio molecular dynamics study of eutectic chloride salt: MgCl2–NaCl–KCl

Ionic liquid materials are viable candidates as a heat transfer fluid (HTF) in a wide range of applications, notably within concentrated solar power (CSP) technology and molten salt reactors (MSRs). For next-generation CSP and MSR technologies that strive for higher power generation efficiency, a HTF with wide liquid phase range and energy storage capabilities is crucial. Studies have shown that eutectic chloride salts exhibit thermal stability at high temperatures, high heat storage capacity, and are less expensive than nitrate and carbonate salts. However, the experimental data needed to fully evaluate the potential of eutectic chloride salts as a HTF contender are scarce and entail large uncertainties. Considering the high cost and potential hazards associated with the experimental methods used to determine the properties of ionic liquids, molecular modeling can be used as a viable alternative resource. In this study, the eutectic ternary chloride salt MgCl 2 –NaCl–KCl is modeled using ab-initio molecular dynamics simulations (AIMDs) in the liquid phase. Using the simulated data, the thermophysical and transport properties of eutectic chloride salt can be calculated: density, viscosity, heat capacity, diffusion coefficient, and ionic conductivity. For an initial model validation, experimental pair-distribution function data were obtained from X-ray total scattering techniques and compared to the theoretical pair-distribution function. Additionally, theoretical viscosity values are compared to experimental viscosity values for a similar system. The results provide a starting foundation for a MgCl 2 –NaCl–KCl model that can be extended to predict other fundamental properties.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

Learning stochastic dynamics and predicting emergent behavior using transformers

We show that a neural network originally designed for language processing can learn the dynamical rules of a stochastic system by observation of a single dynamical trajectory of the system, and can accurately predict its emergent behavior under conditions not observed during training. We consider a lattice model of active matter undergoing continuous-time Monte Carlo dynamics, simulated at a density at which its steady state comprises small, dispersed clusters. We train a neural network called a transformer on a single trajectory of the model. The transformer, which we show has the capacity to represent dynamical rules that are numerous and nonlocal, learns that the dynamics of this model consists of a small number of processes. Forward-propagated trajectories of the trained transformer, at densities not encountered during training, exhibit motility-induced phase separation and so predict the existence of a nonequilibrium phase transition. Transformers have the flexibility to learn dynamical rules from observation without explicit enumeration of rates or coarse-graining of configuration space, and so the procedure used here can be applied to a wide range of physical systems, including those with large and complex dynamical generators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Inertial dynamics of an interface with interfacial mass flux: Stability and flow fields’ structure, inertial stabilization mechanism, degeneracy of Landau’s solution, effect of energy fluctuations, and chemistry-induced instabilities

This work focuses on the long-standing problem of inertial dynamics of an interface with interfacial mass flux and reports new mechanisms for the interface stabilization and destabilization. The interface is a phase boundary separating fluids of different densities and having interfacial mass flux. To analyze the interface dynamics from a far field, we develop and apply the general matrix method to rigorously solve the boundary value problem involving the governing equations in the fluid bulk and the boundary conditions at the interface and at the outside boundaries of the domain. We find the fundamental solutions for the linearized system of equations and analyze the interplay of interface stability with flow fields’ structure by directly linking rigorous mathematical attributes to physical observables. We find that the interface is stable when the dynamics conserves the fluxes of mass, momentum, and energy; the stabilization is due to an inertial mechanism causing small oscillations of the interface velocity. In the classic Landau’s dynamics, the postulate of perfect constancy of the interface velocity leads to the development of Landau–Darrieus instability. This destabilization is also linked to the imbalance of the perturbed energy at the interface. The classic Landau’s solution is found to have degeneracy; lifting of the degeneracy may lead to singularity and self-similar dynamics. Our results compare well with traditional theories of combustion and propose new experiments to study the dynamics of the interface and the flow fields in combustible systems. We further conduct reactive molecular dynamics simulations to elucidate the complexity of chemical processes, to study the destabilizing effect of energy fluctuations on the interface stability, and to illustrate the chemistry-induced instabilities. In summary, we identify the extreme sensitivity of the interface dynamics to the interfacial boundary conditions, including the formal properties of fundamental solutions and the qualitative and quantitative properties of the flow fields. This provides new opportunities for studies, diagnostics, and control of multiphase flows in a broad range of processes in nature and technology.

42 ENGINEERING↗
DOE OSTI2024

Using a multistate mapping approach to surface hopping to predict the ultrafast electron diffraction signal of gas-phase cyclobutanone

Using the recently developed multistate mapping approach to surface hopping (multistate MASH) method combined with SA(3)-CASSCF(12,12)/aug-cc-pVDZ electronic structure calculations, the gas-phase isotropic ultrafast electron diffraction (UED) of cyclobutanone is predicted and analyzed. After excitation into the n-3s Rydberg state (S2), cyclobutanone can relax through two S2/S1 conical intersections, one characterized by compression of the CO bond and the other by dissociation of the α–CC bond. Subsequent transfer into the ground state (S0) is then achieved via two additional S1/S0 conical intersections that lead to three reaction pathways: α ring-opening, ethene/ketene production, and CO liberation. The isotropic gas-phase UED signal is predicted from the multistate MASH simulations, allowing for a direct comparison to the experimental data. This work, which is a contribution to the cyclobutanone prediction challenge, facilitates the identification of the main photoproducts in the UED signal and thereby emphasizes the importance of dynamics simulations for the interpretation of ultrafast experiments.

Chemistry↗
DOE OSTI2021

Nonlinear Shear Rheology of Entangled Polymer Rings

Steady-state shear viscosity η($\bar{γ}$) of unconcatenated ring polymer melts as a function of the shear rate $\bar{γ}$ is studied by a combination of experiments, simulations, and theory. Experiments using polystyrenes with Z ≈ 5 and Z ≈ 11 entanglements indicate weaker shear thinning for rings compared to linear polymers exhibiting power law scaling of shear viscosity η ~ $\bar{γ}$ –0.56 ± 0.02 , independent of chain length, for Weissenberg numbers up to about 10 2 . Nonequilibrium molecular dynamics simulations using the bead-spring model reveal a similar behavior with η ~ $\bar{γ}$ -0.57 ± 0.08 for 4 ≤ Z ≤ 57. Viscosity decreases with chain length for high $\bar{γ}$. In our experiments, we see the onset of this regime, and in simulations, which we extended to Wi ~ 10 4 , the nonuniversality is fully developed. In addition to a naive scaling theory yielding for the universal regime η ~ $\bar{γ}$ –0.57 , we developed a novel shear slit model explaining many details of observed conformations and dynamics as well as the chain length-dependent behavior of viscosity at large $\bar{γ}$. The signature feature of the model is the presence of two distinct length scales: the size of tension blobs and much larger thickness of a shear slit in which rings are self-consistently confined in the velocity gradient direction and which is dictated by the size of a chain section with relaxation time 1/$\bar{γ}$. These two length scales control the two normal stress differences. In this model, the chain length-dependent onset of nonuniversal behavior is set by tension blobs becoming as small as about one Kuhn segment. This model explains the approximate applicability of the Cox–Merz rule for ring polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Tight-Binding-Based Method for Simulations of Complex Materials with Accompanying Database

The NRL-Tight-Binding (NRL-TB) method has been applied successfully to many monatomic, binary, and a few ternary systems. The method consists of a fit to Density Functional Theory (DFT) results and has transferability beyond the fitted DFT database including performing tight-binding molecular dynamics simulations. We are working on a new website with a detailed guide of how to use the method in performing the fit to DFT results (Fitting Code) and obtaining non-orthogonal TB parameters expressed in a polynomial form as environment-dependent Slater-Koster parameters (on-site terms), and neighbor distance-dependent Hamiltonian and Overlap terms. These parameters are then used in a Static Code to verify or make predictions for large systems often beyond the computational capabilities of standard DFT codes. Our starting point was a website developed at NRL some years ago which NRL no longer supports. We have now been augmenting that website with new examples of expanding the original capabilities of the method such as incorporating in the codes f-electron states. Our website (esd.cos.gmu.edu/tb) contains two separate branches: One named NRL-TB with the full documentation of the method including source codes and examples. The other branch, named ESD-dB, is a library of electronic structure calculations for all the monatomic elements (a subset of the elements also found in a recent book by the author of this proposal. See reference 22) and an approximately 700 hundred compounds in specific crystal structures. We discuss below the progress made so far in improving and expanding the capabilities of our TB method and the plans for further extensions of our approach.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Assessment of software methods for estimating protein-protein relative binding affinities

A growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (ΔΔG) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (ΔΔG < -0.5 kcal/mol) with high (83–98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Exploring the formation of gold/silver nanoalloys with gas-phase synthesis and machine-learning assisted simulations

While nanoalloys are of paramount scientific and practical interest, the main processes leading to their formation are still poorly understood. Key structural features in the alloy systems, including the crystal phase, chemical ordering, and morphology, are challenging to control at the nanoscale, making it difficult to extend their use to industrial applications. Here, in this contribution, we focus on the gold/silver system that has two of the most prevalent noble metals and combine experiments with simulations to uncover the formation mechanisms at the atomic level. Nanoparticles were produced using a state-of-the-art inert-gas aggregation source and analyzed using transmission electron microscopy and energy-dispersive X-ray spectroscopy. Machine-learning-assisted molecular dynamics simulations were employed to model the crystallization process from liquid droplets to nanocrystals. Our study finds a preponderance of nanoparticles with five-fold symmetric morphology, including icosahedra and decahedra which is consistent with previous results on mono-metallic nanoparticles. However, we observed that gold atoms, rather than silver atoms, segregate at the surface of the obtained nanoparticles for all the considered alloy compositions. These segregation tendencies are in contrast to previous studies and have consequences on the crystallization dynamics and the subsequent crystal ordering. We finally showed that the underpinning of this surprising segregation dynamics is due to charge transfer and electrostatic interactions rather than surface energy considerations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Effect of an oxide layer on high velocity impact of tantalum particles characterized using molecular dynamics

Micro-cold spray, also commonly referred to as the aerosol deposition method or vacuum kinetic spraying, is a process for depositing nanostructured films of metals and ceramics by impacting solid nanoparticles at high velocity onto a substrate at room temperature and at low pressures. Tantalum films deposited by micro-cold spray are desirable because tantalum is non-reactive to certain molten metals and can therefore serve as a non-reactive barrier coating. However, unlike most other metals deposited by micro-cold spray, tantalum quickly forms an oxide layer when exposed to air. In the deposition of films using traditional cold spray with much larger particles, native oxides must fracture for particle–substrate bonding to occur. Furthermore, the fraction of the particle that is oxidized in micro-cold spray and the strain rates experienced by the particles upon impact are both much larger than in cold spray, suggesting that the role of the oxide on film deposition may be distinct. The effect of this oxide layer on particle deformation and adherence to a substrate under high strain rates is studied using molecular dynamics simulations. The effect of oxide layer thickness, particle diameter, and impact velocity are examined.

42 ENGINEERING↗
DOE OSTI2022

Development of machine learning framework for interface force closures based on bubble tracking data

Interfacial force closures in the two-fluid model play a critical role for the predictive capabilities of void fraction distribution. However, the practices of interfacial force modeling have long been challenged by the inherent physical complexity of the two-phase flows. The rapidly expanding computational capabilities in the recent years have made high-fidelity data from the interface-captured direct numerical simulation become more available, and hence potential for data-driven interfacial force modeling has prevailed. In this work, we established a data-driven modeling framework integrated to the HZDR multiphase Eulerian-Eulerian framework for computational fluid dynamics simulations. The data-driven framework is verified in a benchmark problem, where a feedforward neural network managed to capture the non-linear mapping between bubble Reynolds number and drag coefficient and reproduce the void distribution resulting from the baseline model in the test case. The second focus is on utilizing the bubble tracking data set to form a closure for the bubble drag in the turbulent bubbly flow, in which the drag coefficient is set to be correlated with the bubble Reynolds number and the Eötvös number. Pseudo-steady state filtering in the Frenet Frame was carried out to obtain the drag coefficient from the turbulent bubbly flow data. The performance of the data-driven drag model is also examined through a case study, where improvement of model’s prediction near-wall is regarded necessary. In conclusion, discussion and further plans of investigation are provided.

42 ENGINEERING↗
DOE OSTI2020

Interference of electrical double layers: Confinement effects on structure, dynamics, and screening of ionic liquids

Ionic liquids are widely used as electrolytes in electronic devices in which they are subject to nanoconfinement within nanopores or nanofilms. Because the intrinsic width of an electrical double layer is on the order of several nanometers, nanoconfinement is expected to fundamentally alter the double layer properties. Furthermore, in confined systems, a large portion of the ions are interfacial, e.g., at the electrode interface, leading to significant deviations of electrostatic screening and ion dynamics as compared to bulk properties. In this article, we systematically investigate the interference between electrical double layers for nanoconfined ionic liquids and the resulting influence on the structure, dynamics, and screening behavior. We perform molecular dynamics simulations for the ionic liquids [BMIm + ][BF$^-_4$] and [BMIm + ][PF$^-_6$] confined between two flat electrodes at systematic separation distances between 1.5 nm and 4.5 nm for both conducting and insulating boundary conditions. We find that while ion dynamics is expectedly slower than in the bulk (by ~2 orders of magnitude), there is an unexpected non-linear trend with the confinement length that leads to a local maximum in dynamic rates at ~3.5–4.5 nm confinement. We show that this nonlinear trend is due to the ion correlation that arises from the interference between opposite double layers. We further evaluate confinement effects on the ion structure and capacitance and investigate the influence of electronic polarization of the ionic liquid on the resulting properties. This systematic evaluation of the connection between electrostatic screening and structure and dynamics of ionic liquids in confined systems is important for the fundamental understanding of electrochemical supercapacitors.

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

Microscopic insights into the solvation of polyethylene glycol chains in water: A machine learning potential approach

Polyethylene glycol (PEG) is a structurally simple, nontoxic, and water-soluble polymer widely utilized in medical and pharmaceutical applications. Notably, when a PEG chain is immersed in water, the surrounding water molecules play a key role in driving conformational changes of this macromolecule. In this study, we explore the solvation behavior of PEG under mechanical strain using molecular dynamics simulations, with an interatomic potential obtained from machine learning. Our focus is on the transition from the favored coil-like conformation to an extended one under external force. Through analyses of radial distribution functions, hydrogen bonding, and solvation dynamics, we uncover how mechanical stretching influences the local hydration environment. Furthermore, we disentangle the enthalpic and entropic contributions to the conformational stability of PEG in water. Surprisingly, our neural network potential model identifies dewetting of PEG C-atoms, and not water H-bonding with PEG O-atoms, as the main enthalpic driving force for the coiling of PEG in water.

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

Comment on “Brownian motion of droplets induced by thermal noise”

We simulate phase separated fluids using the Cahn-Hillard fluctuating hydrodynamic (CH-FHD) model and measure the statistical properties of capillary waves generated by thermal fluctuations. Our measurements are in good agreement with stochastic lubrication theory and molecular dynamics simulations but differ significantly from recent CH-FHD results by Zhang et al. [Phys. Rev. E 109, 024208 (2024)2470-004510.1103/PhysRevE.109.024208]. Specifically, we find that capillary wave statistics at thermodynamic equilibrium are independent of transport properties, namely viscosity and species diffusion.

Bell, J B↗
DOE OSTI2020

Experimental and Theoretical Studies of Ultrafast Vibrational Energy Transfer Dynamics in Energetic Materials

Energy transfer through anharmonically-coupled vibrations influences the earliest chemical steps in shockwave-induced detonation in energetic materials. A mechanistic description of vibrational energy transfer is therefore necessary to develop predictive models of energetic material behavior. We performed transient broadband infrared spectroscopy on hundreds of femtoseconds to hundreds of picosecond timescales as well as density functional theory and molecular dynamics simulations to investigate the evolution of vibrational energy distribution in thin film samples of pentaerythritol tetranitrate (PETN) , 1,3,5 - trinitroperhydro - 1,3,5 - triazine (RDX) , and 2,4,6 - triamino 1,3,5 - trinitrobenzene (TATB). Experimental results show dynamics on multiple timescales, providing strong evidence for coupled vibrations in these systems, as well as material-dependent evolution on tens to hundreds of picosecond timescales. Theoretical results also reveal pathways and distinct timescales for energy transfer through coupled vibrations in the three investigated materials, providing further insight into the mechanistic underpinnings of energy transfer dynamics in energetic material sensitivity.

36 MATERIALS SCIENCE↗
DOE OSTI2021

EZFF: Python library for multi-objective parameterization and uncertainty quantification of interatomic forcefields for molecular dynamics

Parameterization of interatomic forcefields is a necessary first step in performing molecular dynamics simulations. This is a non-trivial global optimization problem involving quantification of multiple empirical variables against one or more properties. We present EZFF, a lightweight Python library for parameterization of several types of interatomic forcefields implemented in several molecular dynamics engines against multiple objectives using genetic-algorithm-based global optimization methods. The EZFF scheme provides unique functionality such as the parameterization of hybrid forcefields composed of multiple forcefield interactions as well as built-in quantification of uncertainty in forcefield parameters and can be easily extended to other forcefield functional forms as well as MD engines.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Emergent electric field induced by dissipative sliding dynamics of domain walls in a Weyl magnet

The dynamic motion of topological defects in magnets induces an emergent electric field, as exemplified by the continuous flow of skyrmion vortices. However, the electrodynamics underlying this emergent field remains poorly understood. In this context, magnetic domain walls—one-dimensional topological defects with two collective modes, sliding and spin-tilt—offer a promising platform for exploration. Here we demonstrate that the dissipative motion of domain walls under oscillatory current excitation generates an emergent electric field. We image domain patterns and quantify the domain-wall length under applied magnetic fields in mesoscopic devices based on the magnetic Weyl semimetal NdAlSi. These devices exhibit exceptionally strong domain-wall scattering and a pronounced emergent electric field, as observed in the imaginary component of the complex impedance. Spin dynamics simulations reveal that domain-wall sliding dominates over spin-tilting, in which the phase delay of the domain-wall motion with respect to the driving force impacts the emergent electric field. Our findings establish domain-wall dynamics as a platform for studying emergent electromagnetic fields and motivate further investigations of the coupled motion of magnetic solitons and conduction electrons.

Yamada, Rinsuke [The University of Tokyo, Japan]↗
DOE OSTI2024

Probing the state of hydrogen in δ − AlOOH at mantle conditions with machine learning potential

Hydrous and nominally anhydrous minerals are a fundamental class of solids of enormous significance to geophysics. They are the water carriers in the deep geological water cycle and impact structural, elastic, plastic, and thermodynamic properties and phase relations in Earth's forming aggregates (rocks). They play a critical role in the geochemical and geophysical processes that shape the planet. Their complexity has prevented predictive calculations of their properties, but progress in materials simulations ushered by machine-learning potentials is transforming this state of affairs. Here, we adopt a hybrid approach that combines deep learning potentials (DPs) with the strongly constrained and appropriately normed meta-generalized gradient approximation functional to simulate a prototypical hydrous system. We illustrate the success of this approach to simulate δ − AlOOH ( δ ) , a phase capable of transporting water down to near the core-mantle boundary of the Earth ( ∼ 2900 km depth and ∼ 135 GPa ) in subducting slabs. A high-throughput sampling of phase space using molecular dynamics simulations with DPs sheds light on the hydrogen-bond behavior and proton diffusion at geophysical conditions. These simulations provide a pathway for a deeper understanding of these crucial components that shape Earth's internal state. Published by the American Physical Society 2024

Luo, Chenxing (ORCID:0000000341166851)↗
DOE OSTI2021

Sierra/SolidMechanics for Target Pulse Analysis

Sierra is a Multiphysics engineering simulation software suite developed and maintained by Sandia National Laboratories. Created for nuclear weapon needs, it is available to national laboratories with a foreign national restriction. The STS Target Systems analysis team is interested in using Sierra to perform explicit dynamics simulations of the tantalum clad tungsten target blocks as a response to sudden temperature changes induced by proton beam pulses. The capability already exists with Abaqus|Explicit, however, there are time and cost limitations due to licensing. The following document serves as a “quick-start” guide to the successful setup, simulation, and post-processing of a target model in Sierra on the OZ3 computing cluster at the Spallation Neutron Source.

97 MATHEMATICS AND COMPUTING↗