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At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Spatiotemporal mapping of mesoscopic liquid dynamics

The study of liquid dynamics at mesoscopic scales is still strewn with difficulty due to limitations in theory and experiment. Historically, significant attention has been given to the analysis of space-time correlation functions and their frequency-Fourier transforms at a few discrete wave numbers. The massive computing power afforded by modern high performance computing clusters and the advent of a wide-angle neutron spin-echo spectrometer, however, have unlocked a more intuitive and fruitful approach to this problem. Using molecular dynamics simulations, here we demonstrate the benefits of spatiotemporally mapping intermediate scattering functions on a dense grid of correlation times and wave numbers. Four model systems are investigated: a Lennard-Jones liquid, a coarse-grained bead-spring polymer, a molten sodium chloride, and a poly(ethylene oxide) melt. We show that the spatiotemporal mapping approach is particularly useful for elucidating the mesoscopic dynamics in these liquids, where several underlying mechanisms, such as molecular relaxations, hydrodynamic modes, and nonhydrodynamic excitations, are potentially at play. Therefore, compared to the traditional method, direct visualization of density space-time correlation functions on two-dimensional color maps permits appraisals of complicated dynamical behavior at mesoscales in a global manner. For example, the scaling relations between space and time for different types of molecular motions can be straightforwardly identified on these plots, without any model-dependent analysis. Additionally, we show how theoretical ideas regarding collective mesoscopic dynamics, such as the classical hydrodynamic theory, the convolution approximation, and a recently proposed phenomenological model, can be discussed in terms of the global features of spatiotemporal maps of intermediate scattering functions. The new perspective offered by the spatiotemporal mapping method should prove useful for the study of liquid dynamics in general.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Experimental and theoretical study of energy transfer in a chromophore triad: What makes modeling dynamics successful?

Simulation of electronic dynamics in realistically large molecular systems is a demanding task that has not yet achieved the same level of quantitative prediction already realized for its static counterpart. This is particularly true for processes occurring beyond the Born–Oppenheimer regime. Non-adiabatic molecular dynamics (NAMD) simulations suffer from two convoluted sources of error: numerical algorithms for dynamics and electronic structure calculations. While the former has gained increasing attention, particularly addressing the validity of ad hoc methodologies, the effect of the latter remains relatively unexplored. Indeed, the required accuracy for electronic structure calculations to reach quantitative agreement with experiment in dynamics may be even more strict than that required for static simulations. In this study, we address this issue by modeling the electronic energy transfer in a donor–acceptor–donor (D–A–D) molecular light harvesting system using fewest switches surface hopping NAMD simulations. In the studied system, time-resolved experimental measurements deliver complete information on spectra and energy transfer rates. Subsequent modeling shows that the calculated electronic transition energies are “sufficiently good” to reproduce experimental spectra but produce over an order of magnitude error in simulated dynamical rates. We further perform simulations using artificially shifted energy gaps to investigate the complex relationship between transition energies and modeled dynamics to understand factors affecting non-radiative relaxation and energy transfer rates.

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

Effects of elastic softening and helium accumulation kinetics on surface morphological evolution of plasma-facing tungsten

Based on a continuous-domain model, capable of accessing the spatiotemporal scales relevant to fuzz formation on the surface of plasma-facing component (PFC) tungsten, we report self-consistent simulation results that elucidate the effects of elastic softening and helium (He) accumulation kinetics on the surface morphological response of PFC tungsten. The model accounts for the softening of the elastic moduli in the near-surface region of PFC tungsten, including both thermal softening at high temperature and softening due to He accumulation upon He implantation. The dependence of the elastic moduli on the He content follows an exponential scaling relation predicted by molecular-dynamics simulations, while the He content in the near-surface region of PFC tungsten evolves according to a first-order saturation kinetics, consistent with experimental and simulation results reported in the literature. We establish that this elastic softening accelerates both nanotendril growth on the PFC surface and the onset of fuzz formation. We also explore the role of the rate of He accumulation to a saturation level in the near-surface region of irradiated tungsten in the onset of fuzz formation. For PFC tungsten surfaces such as W(110) where, under typical irradiation conditions, the characteristic time scale for stress-driven surface diffusion is comparable to the characteristic time scale for He accumulation, we find that accelerating the rate of He accumulation accelerates the growth rate of nanotendrils emanating from the surface. Additionally, we present a systematic parametric study of the PFC surface morphological response to explore its dependence on the He accumulation kinetics that is controlled by the irradiation conditions for low-energy implantation. Finally, we introduce an incubation time for nanotendril growth on the PFC surface, a concept equivalent to that of incubation fluence discussed in the literature, to predict and explain the minimum exposure time required to observe fuzz formation on PFC tungsten surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Coalescence and splashing threshold for head-on collisions of liquid metal nanodroplets

Head-on collisions of liquid metal nanodroplets in a vacuum are investigated through molecular dynamics simulations in order to determine the transition threshold between the coalescing and splashing regimes for six different materials (aluminum, calcium, cerium, gold, platinum, and tin). Droplets of various sizes and initial speeds are simulated, and it is found that the Reynolds and Ohnesorge numbers are able to predict the transition between the coalescing and splashing regimes. An energy balance for coalescing droplets shows that the initial energy is mainly converted to thermal energy increasing the temperature of the combined droplets by several hundred to several thousand kelvin depending on the material, and this result is confirmed in the simulations. Furthermore, when splashing occurs, the number of smaller droplets formed and the spreading rate are found to be dependent on the initial size and initial speed of the original droplets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Solvated Electrons Have Multiple Personalities in Molten Salts

P-Crosscut: Solvated Electrons Have Multiple Personalities in Molten Salts [EFRC – MSEE] Alejandro Ramos-Ballesteros;1 Hung H. Nguyen;2 Kazuhiro Iwamatsu;3 Santanu Roy;4 Vyacheslav Bryantsev;4 Michael E. Woods;1 Ruchi Gakhar;1 Phillip Halstenberg;4 Bobby Layne;5 Jay A. LaVerne;6 Claudio J. Margulis:2* and James F. Wishart5* 1Idaho National Laboratory; 2The University of Iowa; 3Hunter College; 4Oak Ridge National Laboratory; 5Brookhaven National Laboratory; 6University of Notre Dame Abstract: The solvated (eS–) is a powerful reducing agent and one of the primary products of molten salt radiolysis. In these extreme high-temperature environments, the eS– can initiate cascades of redox processes that significantly alter the physical and chemical properties of a molten salt, posing challenges for molten salt reactor (MSR) performance and longevity. Consequently, mastering the fundamental behavior of the eS– could enable the design of specific molten salt mixtures with tailored Lewis acidities for controlling the speciation and chemical reactivity of the eS–, thereby mitigating its overall impact on MSR technologies. Here, we use time-resolved electron pulse radiolysis techniques for determining chemical kinetics and transient absorption spectra, combined with ab initio molecular dynamics simulations to explore the influence of multivalent metal cations on the fundamental speciation and distribution of eS– coordination environments.

38 - RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCL↗
DOE OSTI2021

Machine learning dielectric screening for the simulation of excited state properties of molecules and materials

Accurate and efficient calculations of absorption spectra of molecules and materials are essential for the understanding and rational design of broad classes of systems. Solving the Bethe–Salpeter equation (BSE) for electron–hole pairs usually yields accurate predictions of absorption spectra, but it is computationally expensive, especially if thermal averages of spectra computed for multiple configurations are required. We present a method based on machine learning to evaluate a key quantity entering the definition of absorption spectra: the dielectric screening. We show that our approach yields a model for the screening that is transferable between multiple configurations sampled during first principles molecular dynamics simulations; hence it leads to a substantial improvement in the efficiency of calculations of finite temperature spectra. We obtained computational gains of one to two orders of magnitude for systems with 50 to 500 atoms, including liquids, solids, nanostructures, and solid/liquid interfaces. Importantly, the models of dielectric screening derived here may be used not only in the solution of the BSE but also in developing functionals for time-dependent density functional theory (TDDFT) calculations of homogeneous and heterogeneous systems. Overall, our work provides a strategy to combine machine learning with electronic structure calculations to accelerate first principles simulations of excited-state properties.

36 MATERIALS SCIENCE↗
DOE OSTI2020

DFT Accurate Interatomic Potential for Molten NaCl from Machine Learning

Molten alkali chloride salts are a critical component in concentrated solar power and nuclear applications. Despite their ubiquity, the extreme chemical reactivity of molten alkali chlorides at high temperatures has presented a significant challenge in characterizing atomic structures and dynamic properties experimentally. In this work, we we investigate molten NaCl by performing high temperature molecular dynamics simulations using a Gaussian Approximation Potential (GAP) trained on Density Functional Theory (DFT) datasets. Our GAP model, trained with a meager 1000 atomic configurations, arrives at near ab initio accuracy with a mean absolute error of 1.5 meV/atom thus enabling fast analysis of high temperature salt properties on large length (5000 ion pairs) and time (> 1ns) scales currently inaccessible to ab initio simulations. Calculated structure factors and diffusion constants from our GAP model simulations show excellent agreement with experiments. Our results indicate that GAP models are able to capture the many-body interactions required to accurately model ionic-systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

tRNA shape is an identity element for an archaeal pyrrolysyl-tRNA synthetase from the human gut

Protein translation is orchestrated through tRNA aminoacylation and ribosomal elongation. Among the highly conserved structure of tRNAs, they have distinguishing features which promote interaction with their cognate aminoacyl tRNA synthetase (aaRS). These key features are referred to as identity elements. In our study, we investigated the tRNA:aaRS pair that installs the 22nd amino acid, pyrrolysine (tRNAPyl:PylRS). Pyrrolysyl-tRNA synthetases (PylRSs) are naturally encoded in some archaeal and bacterial genomes to acylate tRNAPyl with pyrrolysine. Their large amino acid binding pocket and poor recognition of the tRNA anticodon have been instrumental in incorporating > 200 noncanonical amino acids. PylRS enzymes can be divided into three classes based on their genomic structure. Two classes contain both an N-terminal and C-terminal domain, however the third class (ΔpylSn) lacks the N-terminal domain. In this study we explored the tRNA identity elements for a ΔpylSn tRNAPyl from Candidatus Methanomethylophilus alvus which drives the orthogonality seen with its cognate PylRS (MaPylRS). From aminoacylation and translation assays we identified five key elements in ΔpylSn tRNAPyl necessary for MaPylRS activity. The absence of a base (position 8) and a G–U wobble pair (G28:U42) were found to affect the high-resolution structure of the tRNA, while molecular dynamic simulations led us to acknowledge the rigidity imparted from the G–C base pairs (G3:C70 and G5:C68).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Atomistic and mesoscale simulations to determine effective diffusion coefficient of fission products in SiC

The silicon carbide (SiC) layer in tristructural isotropic (TRISO) particles serves as the barrier to prevent escape of fission products produced in the fuel kernel. Knowing the diffusion coefficient of fission products through SiC is critical to determining whether fission gas can escape from the particle. It has been observed in experiments that Ag accumulated in grain boundaries and triple junctions in SiC. It is hypothesized that grain boundary diffusion is the primary pathway by which fission products penetrate the SiC layer. In this report, the effective diffusion coefficient of the fission product Ag through the grain boundary network is calculated using a combination of atomistic and phase-field methods. The grain boundary diffusion coefficient is calculated using molecular dynamics simulations. The bulk diffusion coefficient is determined using a combination of density functional theory and nudged elastic band methods. An effective diffusion coefficient is calculated, accounting for the grain structure using a phase-field method. The effective diffusion coefficient will be incorporated into Bison and fission product release calculations are compared to available experimental data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Graph-EAM: An Interpretable and Efficient Graph Neural Network Potential Framework

The development of deep learning interatomic potentials has enabled efficient and accurate computations in quantum chemistry and materials science, circumventing computationally expensive ab initio calculations. However, the huge number of learnable parameters in deep learning models and their complex architectures hinder physical interpretability and affect the robustness of the derived potential. In this work, we propose graph-EAM, a lightweight graph neural network (GNN) inspired by the empirical embedded atom method to model the interatomic potential of single-element structures. Four material systems: platinum, niobium, silicon, and amorphous-carbon, for which quantum simulation data sets are publicly available, are examined to demonstrate that graph-EAM can achieve high energy and force prediction accuracy-comparable or better than existing state-of-the-art machine learning models-with much fewer parameters. It is also shown that the explicit inclusion of the angular information via three-body atomic density increases the prediction accuracy. In conclusion, the accuracy and efficiency of potentials obtained from graph-EAM can help accelerate the molecular dynamics simulation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Reaction-induced departures from continuum Navier–Stokes turbulence

Reactive hydrodynamic turbulence is an inherently multiscale phenomenon, characterized by the separation between energy-containing, viscous, and molecular length and time scales. The separation between the viscous scale (the Kolmogorov scale) and the molecular mean free path ostensibly justifies a macroscopic description of reactive turbulence via the Navier–Stokes (NS) equations. However, here we use molecular-level simulations to demonstrate that exothermic bimolecular reactions can cause the NS description of turbulence to break down in the near-continuum regime. Sufficiently energetic heat-releasing reactive collisions strongly distort the Maxwell–Boltzmann velocity distribution function, modifying not only the macroscopic chemical rate law but the kinetic-energy-transfer processes as well. This translational nonequilibrium ultimately introduces significant departures from the NS description in the kinetic energy spectra at scales orders of magnitude larger than both the molecular mean free path and the Kolmogorov length scale. These departures prove substantial enough to meaningfully alter integrated quantities, including the overall turbulence kinetic energy itself.

molecular gas dynamics↗
DOE OSTI2021

Elevating density functional theory to chemical accuracy for water simulations through a density-corrected many-body formalism

Abstract Density functional theory (DFT) has been extensively used to model the properties of water. Albeit maintaining a good balance between accuracy and efficiency, no density functional has so far achieved the degree of accuracy necessary to correctly predict the properties of water across the entire phase diagram. Here, we present density-corrected SCAN (DC-SCAN) calculations for water which, minimizing density-driven errors, elevate the accuracy of the SCAN functional to that of “gold standard” coupled-cluster theory. Building upon the accuracy of DC-SCAN within a many-body formalism, we introduce a data-driven many-body potential energy function, MB-SCAN(DC), that quantitatively reproduces coupled cluster reference values for interaction, binding, and individual many-body energies of water clusters. Importantly, molecular dynamics simulations carried out with MB-SCAN(DC) also reproduce the properties of liquid water, which thus demonstrates that MB-SCAN(DC) is effectively the first DFT-based model that correctly describes water from the gas to the liquid phase.

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

Amplifying Nanoparticle Reinforcement through Low Volume Topologically Controlled Chemical Coupling

Here, we present a streamlined method to covalently bond hydroxylated carbon nanotubes (CNOH) within a polyphenol matrix, all achieved through a direct, solvent-free process. Employing an extremely small concentration of CNOH (0.01% w/w) along with topologically contrasting linkers led to a maximum of 5-fold increase in modulus and a 25% enhancement in tensile strength compared to the unaltered matrix, an order of magnitude greater reinforcement (w/w) compared to state-of-the-art melt-processed nanocomposites. Through dynamic mechanical analysis, low field solid-state nuclear magnetic resonance spectroscopy, and molecular dynamics simulations, we uncovered the profound influence of linker’s conformational degrees of freedom on the segmental dynamics and therefore the material’s properties.

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

Stereoselective recognition of morphine enantiomers by μ -opioid receptor

Stereospecific recognition of chiral molecules plays a crucial role in biological systems. The μ-opioid receptor (MOR) exhibits binding affinity towards (-)-morphine, a well-established gold standard in pain management, while it shows minimal binding affinity for the (+)-morphine enantiomer, resulting in a lack of analgesic activity. Understanding how MOR stereoselectively recognizes morphine enantiomers has remained a puzzle in neuroscience and pharmacology for over half-a-century due to the lack of direct observation techniques. To unravel this mystery, we constructed the binding and unbinding processes of morphine enantiomers with MOR via molecular dynamics simulations to investigate the thermodynamics and kinetics governing MOR's stereoselective recognition of morphine enantiomers. Our findings reveal that the binding of (-)-morphine stabilizes MOR in its activated state, exhibiting a deep energy well and a prolonged residence time. In contrast, (+)-morphine fails to sustain the activation state of MOR. Furthermore, the results suggest that specific residues, namely D114 2.50 and D147 3.32 , are deprotonated in the active state of MOR bound to (-)-morphine. This work highlights that the selectivity in molecular recognition goes beyond binding affinities, extending into the realm of residence time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Using limited neural networks to assess relative mechanistic influence on shock heating in granular solids

The rapid compaction of granular media results in localized heating that can induce chemical reactions, phase transformations, and melting. However, there are numerous mechanisms in play that can be dependent on a variety of microstructural features. Machine learning techniques such as neural networks offer a ubiquitous method to develop models for physical processes. Limiting what kind of microstructural information is used as an input and assessing normalized changes in network error, the relative importance of different mechanisms can be inferred. Here we utilize binned, initial density information as network inputs to predict local shock heating in a granular high explosive trained from large-scale molecular dynamics simulations. In this study, the spatial extent of the density field used in the network is altered to assess the importance and relevant length scales of the physical mechanisms in play, where different microstructural features result in different predictive capabilities.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Simulations of classical three-body thermalization in one dimension

One-dimensional systems, such as nanowires or electrons moving along strong magnetic field lines, have peculiar thermalization physics. The binary collision of pointlike particles, typically the dominant process for reaching thermal equilibrium in higher-dimensional systems, cannot thermalize a 1D system. We study how dilute classical 1D gases thermalize through three-body collisions. We consider a system of identical classical point particles with pairwise repulsive inverse power-law potential V ij ∝ 1/|x i –x j | n or the pairwise Lennard-Jones potential. Using Monte Carlo methods, we compute a collision kernel and use it in the Boltzmann equation to evolve a perturbed thermal state with temperature T toward equilibrium. We explain the shape of the kernel and its dependence on the system parameters. Additionally, we implement molecular dynamics simulations of a many-body gas and show agreement with the Boltzmann evolution in the low-density limit. For the inverse power-law potential, the rate of thermalization is proportional to ρ 2 ⁢T$\frac{1}{2}$ – $\frac{1}{n}$, where ρ is the number density. Furthermore, the corresponding proportionality constant decreases with increasing n.

1-dimensional systems↗
DOE OSTI2024

Phase Diagram of Nuclear Pastas in Neutron Star Crusts

Two neural networks were trained to predict, respectively, the Euler characteristic and the curvature of nuclear pastas in neutron star crust conditions generated by molecular dynamics simulations of neutron star matter with 0.1 < x < 0.5, 0.040 fm−3 < ρ < 0.085 fm−3 (0.68 × 1014 g/cm3 < ρ < 1.43 × 1014 g/cm3), and 0.2 MeV < T < 4.0 MeV, where x is proton content, the density is ρ, and the temperature is T. The predictions of the two networks were combined to determine the nuclear pasta phase that is thermodynamically stable at a given x, ρ, and T, and a three-dimensional phase diagram that extrapolated slightly the regions of existing molecular dynamics data was computed. The jungle gym and anti-jungle gym structures are prevalent at high temperature and low density, while the anti-jungle gym and anti-gnocchi structures dominate at high temperature and high density. A diversity of structures exist at low temperatures and intermediate density and proton content. The trained models used in this work are open access and available at a public repository to promote comparison to pastas obtained with other models.

Muñoz, Jorge A. (ORCID:0000000294870079)↗
DOE OSTI2021

Crystal Structure and Preparation of Li7La3Zr2O12 (LLZO) Solid-State Electrolyte and Doping Impacts on the Conductivity: An Overview

As an essential part of solid-state lithium-ion batteries, solid electrolytes are receiving increasing interest. Among all solid electrolytes, garnet-type Li7La3Zr2O12 (LLZO) has proven to be one of the most promising electrolytes because of its high ionic conductivity at room temperature, low activation energy, good chemical and electrochemical stability, and wide potential window. Since the first report of LLZO, extensive research has been done in both experimental investigations and theoretical simulations aiming to improve its performance and make LLZO a feasible solid electrolyte. These include developing different methods for the synthesis of LLZO, using different crucibles and different sintering temperatures to stabilize the crystal structure, and adopting different methods of cation doping to achieve more stable LLZO with a higher ionic conductivity and lower activation energy. It also includes intensive efforts made to reveal the mechanism of Li ion movement and understand its determination of the ionic conductivity of the material through molecular dynamic simulations. Nonetheless, more insightful study is expected in order to obtain LLZO with a higher ionic conductivity at room temperature and further improve chemical and electrochemical stability, while optimal multiple doping is thought to be a feasible and promising route. This review summarizes recent progress in the investigations of crystal structure and preparation of LLZO, and the impacts of doping on the lithium ionic conductivity of LLZO.

Raju, Md Mozammal (ORCID:0000000319040523)↗