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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 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Atomistic modelling of scattering data in the Collaborative Computational Project for Small Angle Scattering (CCP-SAS)

The capabilities of current computer simulations provide a unique opportunity to model small-angle scattering (SAS) data at the atomistic level, and to include other structural constraints ranging from molecular and atomistic energetics to crystallography, electron microscopy and NMR. This extends the capabilities of solution scattering and provides deeper insights into the physics and chemistry of the systems studied. Realizing this potential, however, requires integrating the experimental data with a new generation of modelling software. To achieve this, the CCP-SAS collaboration (http://www.ccpsas.org/) is developing open-source, high-throughput and user-friendly software for the atomistic and coarse-grained molecular modelling of scattering data. Robust state-of-the-art molecular simulation engines and molecular dynamics and Monte Carlo force fields provide constraints to the solution structure inferred from the small-angle scattering data, which incorporates the known physical chemistry of the system. The implementation of this software suite involves a tiered approach in which GenApp provides the deployment infrastructure for running applications on both standard and high-performance computing hardware, and SASSIE provides a workflow framework into which modules can be plugged to prepare structures, carry out simulations, calculate theoretical scattering data and compare results with experimental data. GenApp produces the accessible web-based front end termed SASSIE-web , and GenApp and SASSIE also make community SAS codes available. Applications are illustrated by case studies: (i) inter-domain flexibility in two- to six-domain proteins as exemplified by HIV-1 Gag, MASP and ubiquitin; (ii) the hinge conformation in human IgG2 and IgA1 antibodies; (iii) the complex formed between a hexameric protein Hfq and mRNA; and (iv) synthetic `bottlebrush' polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Constraint methods that accelerate free-energy simulations of biomolecules

Atomistic molecular dynamics simulations of biomolecules are critical for generating narratives about biological mechanisms. The power of atomistic simulations is that these are physics-based methods that satisfy Boltzmann’s law, so they can be used to compute populations, dynamics, and mechanisms. But physical simulations are computationally intensive and do not scale well to the sizes of many important biomolecules. One way to speed up physical simulations is by coarse-graining the potential function. Another way is to harness structural knowledge, often by imposing spring-like restraints. But harnessing external knowledge in physical simulations is problematic because knowledge, data, or hunches have errors, noise, and combinatoric uncertainties. Here, we review recent principled methods for imposing restraints to speed up physics-based molecular simulations that promise to scale to larger biomolecules and motions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

pnnl/galas

Codebase for analyzing large atomistic simulation results using graph analytics. Analysis of large molecular simulations is difficult due to size and memory constrains in commonly used analysis software. This code was developed to analyze an ~8 million atom polycrystalline Al system under shear, with a particular emphasis on identifying defect structures. This code applies graph theory to reduce the system to components of interest and applies associated algorithms to characterize these components.

Pope, Jenna (Bilbrey)↗
DOE OSTI2020

Simulations of activities, solubilities, transport properties, and nucleation rates for aqueous electrolyte solutions

This article reviews recent molecular simulation studies of "collective" properties of aqueous electrolyte solutions, specifically free energies and activity coefficients, solubilities, nucleation rates of crystals, and transport coefficients. These are important fundamental properties for biology and geoscience, but also relevant for many technological applications. Their determination from molecular-scale calculations requires large systems and long sampling times, as well as specialized sampling algorithms. As a result, such properties have not typically been taken into account during optimization of force field parameters; thus, they provide stringent tests for the transferability and range of applicability of proposed molecular models. There has been significant progress on simulation algorithms to enable the determination of these properties with good statistical uncertainties. Comparisons of simulation results to experimental data reveal deficiencies shared by many commonly used models. Moreover, there appear to exist specific tradeoffs within existing modeling frameworks, so that good prediction of some properties is linked to poor prediction for specific other properties. For example, non-polarizable models that utilize full charges on the ions generally fail to predict accurately both activity coefficients and solubilities; the concentration dependence of viscosity and diffusivity for these models is also incorrect. Scaled-charge models improve the dynamic properties and could also perform well for solubilities, but fail in the prediction of nucleation rates. Even models that do well at room temperature for some properties generally fail to capture their experimentally observed temperature dependence. Finally, the main conclusion from the present review is that qualitatively new physics will need to be incorporated in future models of electrolyte solutions to allow description of collective properties for broad ranges of concentrations, temperatures, and solvent conditions.

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

First principles simulation of reacting hypersonic flow over a blunt wedge

This article presents molecular-level analysis of a reactive, near-continuum, Mach 21 nitrogen flow over a blunt wedge using the direct molecular simulation (DMS) method. The flow conditions lead to internal energy excitation and dissociation in the flow field, resulting in thermal and chemical nonequilibrium in the flow. Thermal nonequilibrium in the vibrational mode is observed to extend to the molecular level, where the vibrational energy distributions at various points in the flow field are observed to be non-Boltzmann. Furthermore, this is the first reactive DMS calculation where the wall is assumed to be isothermal and full momentum accommodation of the particles is enforced, hence incorporating viscous wall effects. Since the DMS method uses a quantum mechanically generated interaction potential as its only modeling input, all thermochemical and transport properties of the flow field can directly be attributed to the ab initio potential energy surface. Using the DMS solution as a benchmark, this article assesses the performance of Navier–Stokes computational fluid dynamics solutions using lower fidelity two-temperature models. Two models are chosen as points of comparison: the well-known Park two-temperature model and the recently developed modified Marrone and Treanor model.

Mechanics↗
DOE OSTI2024

Elucidating the formation mechanisms of zeolites using data-driven modelling and in-situ characterization

Zeolites are the main solid catalysts used by the chemical industry. The use of zeolites in separations and as shape selective catalysts requires control of the width and connectivity of their pores. 235 distinct zeolite frameworks have been synthesized to date, of over 2 million that have been proposed. Recent work indicates that the limitation is in large part kinetic: new synthetic pathways are required to access new zeolites. Organic cations are used to direct the synthesis towards specific zeolites. However, the molecular mechanisms by which cations direct the nucleation towards specific zeolites is not known. Elucidating these mechanisms is key to realize new zeolites for catalysis and separations, and is the focus of this project. This project developed and implemented a synergistic, data-driven computational and experimental approach to resolve the molecular pathways of nucleation, growth, and polymorph selection of zeolites and the role of organic cations in directing their formation. The project developed computationally efficient and accurate models for the study of the nucleation and growth of pure silica zeolites in molecular simulations, using machine learning with data from experiments. Simulations with these models were integrated with scanning tunneling electron microscopy, computer vision, and deep learning to unveil the molecular pathways of formation of a zeolite. Of particular interest in this project was to elucidate the role of amorphous precursors in the nucleation of the zeolite. Previous experiments indicate that zeolites are born within non-crystalline aggregates in which the silicates and organic cations have local and medium range order similar to that of the zeolite. The organic cations that direct the formation of zeolites and those that direct the formation of ordered mesoporous silicas are similar. We hypothesized that the frustrated attraction that for large organic cations leads to the formation of stable mesophases that direct the synthesis of mesoporous silicas, could promote the formation of metastable mesophases that can assist in the nucleation and polymorph selection of zeolites. The simulations resolved how structure directing agents build crystalline order and showed that mesoscopic pre-ordering occurs is not required to facilitate the nucleation of zeolites, because the synthesis occurs at high driving forces, where the barriers for nucleation are negligible. This project unveiled that polymorph selection in zeolite synthesis occurs after nucleation, opening a distinct area of control through the kinetics of growth and not through nucleation barriers.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Computational efficiency and Amdahl’s law for the adaptive resolution simulation technique

Here, we discuss the computational performance of the adaptive resolution technique in molecular simulation when it is compared with equivalent full coarse-grained and full atomistic simulations. We show that an estimate of its efficiency, within 10%–15% accuracy, is given by the Amdahl’s Law adapted to the specific quantities involved in the problem. The derivation of the predictive formula is general enough that it may be applied to the general case of molecular dynamics approaches where a reduction of degrees of freedom in a multi scale fashion occurs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Properties of liquid clusters in large-scale molecular dynamics nucleation simulations

We have performed large-scale Lennard-Jones molecular dynamics simulations of homogeneous vapor-to-liquid nucleation, with 10{sup 9} atoms. This large number allows us to resolve extremely low nucleation rates, and also provides excellent statistics for cluster properties over a wide range of cluster sizes. The nucleation rates, cluster growth rates, and size distributions are presented in Diemand et al. [J. Chem. Phys. 139, 74309 (2013)], while this paper analyses the properties of the clusters. We explore the cluster temperatures, density profiles, potential energies, and shapes. A thorough understanding of the properties of the clusters is crucial to the formulation of nucleation models. Significant latent heat is retained by stable clusters, by as much as ΔkT = 0.1ε for clusters with size i = 100. We find that the clusters deviate remarkably from spherical—with ellipsoidal axis ratios for critical cluster sizes typically within b/c = 0.7 ± 0.05 and a/c = 0.5 ± 0.05. We examine cluster spin angular momentum, and find that it plays a negligible role in the cluster dynamics. The interfaces of large, stable clusters are thinner than planar equilibrium interfaces by 10%−30%. At the critical cluster size, the cluster central densities are between 5% and 30% lower than the bulk liquid expectations. These lower densities imply larger-than-expected surface areas, which increase the energy cost to form a surface, which lowers nucleation rates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Statistical Inference for Big Data Problems in Molecular Biophysics

We highlight the role of statistical inference techniques in providing biological insights from analyzing long time-scale molecular simulation data. Technologi- cal and algorithmic improvements in computation have brought molecular simu- lations to the forefront of techniques applied to investigating the basis of living systems. While these longer simulations, increasingly complex reaching petabyte scales presently, promise a detailed view into microscopic behavior, teasing out the important information has now become a true challenge on its own. Mining this data for important patterns is critical to automating therapeutic intervention discovery, improving protein design, and fundamentally understanding the mech- anistic basis of cellular homeostasis.

Ramanathan, Arvind↗
DOE OSTI2016

Dynamic acid/base equilibrium in single component switchable ionic liquids and consequences on viscosity

The deployment of transformational non-aqueous CO 2 -capture solvent systems is encumbered by high viscosity even at intermediate uptakes. Using single-molecule CO 2 binding organic liquids as a prototypical example, we identify the key molecular features controlling bulk liquid viscosity and CO 2 uptake kinetics. Fast uptake kinetics arise from close proximity of the alcohol and amine sites that are involved in CO 2 binding. This process results in the concerted formation of a Zwitterion containing both an alkylcarbonate and a protonated amine. The hydrogen bonding between the two functional groups ultimately determines the solution viscosity. Based on molecular simulation, this work reveals options to significantly reduce viscosity with molecular modifications that shift the proton transfer equilibrium towards a neutral acid/amine species as opposed to the ubiquitously accepted Zwitterionic state. Lastly, the molecular design concepts proposed here, for the alkyl-carbonate systems, are readily extensible to other CO 2 capture technologies, such as the carbamate- or imidazole-based solvent chemistries.

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

Liquid–Vapor Phase Equilibrium in Molten Aluminum Chloride (AlCl 3 ) Enabled by Machine Learning Interatomic Potentials

Molten salts are promising candidates in numerous clean energy applications, where knowledge of thermophysical properties and vapor pressure across their operating temperature ranges is critical for safe operations. Due to challenges in evaluating these properties using experimental methods, fast and scalable molecular simulations are essential to complement the experimental data. In this study, we developed machine learning interatomic potentials (MLIP) to study the AlCl 3 molten salt across varied thermodynamic conditions (T = 473–613 K and P = 2.7–23.4 bar), which allowed us to predict temperature-surface tension correlations and liquid–vapor phase diagram from direct simulations of two-phase coexistence in this molten salt. Two MLIP architectures, a Kernel-based potential and neural network interatomic potential (NNIP), were considered to benchmark their performance for AlCl 3 molten salt using experimental structure and density values. The NNIP potential employed in two-phase equilibrium simulations yields the critical temperature and critical density of AlCl 3 that are within 10 K (∼3%) and 0.03 g/cm 3 (∼7%) of the reported experimental values. An accurate correlation between temperature and viscosities is obtained as well. In doing so, we report that the inclusion of low-density configurations in their training is critical to more accurately represent the AlCl 3 system across a wide phase-space. The MLIP trained using PBE-D3 functional in the ab initio molecular dynamics (AIMD) simulations (120 atoms) also showed close agreement with experimentally determined molten salt structure comprising Al 2 Cl 6 dimers, as validated using Raman spectra and neutron structure factor. Furthermore, the PBE-D3 as well as its trained MLIP showed better liquid density and temperature correlation for AlCl 3 system when compared to several other density functionals explored in this work. Overall, the demonstrated approach to predict temperature correlations for liquid and vapor densities in this study can be employed to screen nuclear reactors-relevant compositions, helping to mitigate safety concerns.

Ab initio molecular dynamics↗
DOE OSTI2021

Molecular aspects of temperature swing solvent extraction for brine desalination using imidazole-based solvents

Solvent extraction desalination has shown to be a promising energy-efficient technique when compared to separation processes usually employed. Despite the remarkable impact of the organic solvent’s molecular characteristics on this process, few studies have been carried out so far to explicitly ascertain the influence of these characteristics on the solubility of water in the solvent. In this report we assess the viability of four different imidazole-based solvents and compare them to an amine compound for solvent extraction desalination of high salinity brines. All-atom molecular simulations of pure solvent and water-solvent bulk mixtures are used to ascertain the influence of the solvents’ molecular structures on solvent–solvent and solvent–water interaction. Our results show that water solubilization in imidazole-based solvents is an endothermic process, while for a secondary amine (diisopropylamine) it is an exothermic process. We show that imidazole-based solvents can be designed to provide characteristics favorable to brine desalination.

42 ENGINEERING↗
DOE OSTI2022

Mechanism of tethered agonist-mediated signaling by polycystin-1

Mutations of polycystin-1 (PC1) are the major cause (85% of cases) of autosomal dominant polycystic kidney disease (ADPKD), which is the fourth leading cause of kidney failure. PC1 is thought to function as an atypical G protein-coupled receptor, yet the mechanism by which PC1 regulates G-protein signaling remains poorly understood. A significant portion of ADPKD mutations of PC1 encode a protein with defects in maturation or reduced function that may be amenable to functional rescue. In this work, we have combined complementary biochemical and cellular assay experiments and accelerated molecular simulations, which revealed an allosteric transduction pathway in activation of the PC1 C-terminal fragment. Our findings will facilitate future rational drug design efforts targeting the PC1 signaling function. Polycystin-1 (PC1) is an important unusual G protein-coupled receptor (GPCR) with 11 transmembrane domains, and its mutations account for 85% of cases of autosomal dominant polycystic kidney disease (ADPKD). PC1 shares multiple characteristics with Adhesion GPCRs. These include a GPCR proteolysis site that autocatalytically divides these proteins into extracellular, N-terminal, and membrane-embedded, C-terminal fragments (CTF), and a tethered agonist (TA) within the N-terminal stalk of the CTF that is suggested to activate signaling. However, the mechanism by which a TA can activate PC1 is not known. Here, we have combined functional cellular signaling experiments of PC1 CTF expression constructs encoding wild type, stalkless, and three different ADPKD stalk variants with all-atom Gaussian accelerated molecular dynamics (GaMD) simulations to investigate TA-mediated signaling activation. Correlations of residue motions and free-energy profiles calculated from the GaMD simulations correlated with the differential signaling abilities of wild type and stalk variants of PC1 CTF. They suggested an allosteric mechanism involving residue interactions connecting the stalk, Tetragonal Opening for Polycystins (TOP) domain, and putative pore loop in TA-mediated activation of PC1 CTF. Key interacting residues such as N3074–S3585 and R3848–E4078 predicted from the GaMD simulations were validated by mutagenesis experiments. Together, these complementary analyses have provided insights into a TA-mediated activation mechanism of PC1 CTF signaling, which will be important for future rational drug design targeting PC1.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Calculation of the Gibbs Free Energy of Solvation and Dissociation of HCl in Water via Monte Carlo Simulations and Continuum Solvation Models

The free energy of solvation and dissociation of hydrogen chloride in water is calculated through a combined molecular simulation quantum chemical approach at four temperatures between T = 300 and 450 K. The free energy is first decomposed into the sum of two components: the Gibbs free energy of transfer of molecular HCl from the vapor to the aqueous liquid phase and the standard-state free energy of acid dissociation of HCl in aqueous solution. The former quantity is calculated using Gibbs ensemble Monte Carlo simulations using either Kohn-Sham density functional theory or a molecular mechanics force field to determine the system’s potential energy. The latter free energy contribution is computed using a continuum solvation model utilizing either experimental reference data or micro-solvated clusters. The predicted combined solvation and dissociation free energies agree very well with available experimental data. CJM was supported by the US Department of Energy,Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences & Biosciences. Pacific Northwest National Laboratory is operated by Battelle for the US Department of Energy.

McGrath, Matthew↗
DOE OSTI2014

Pressure-induced transformations in amorphous silicon: A computational study

We study the transformations between amorphous phases of Si through molecular simulations using the environment dependent interatomic potential (EDIP) for Si. Our results show that upon pressure, the material undergoes a transformation from the low density amorphous (LDA) Si to the high density amorphous (HDA) Si. This transformation can be reversed by decompressing the material. This process, however, exhibits clear hysteresis, suggesting that the transformation LDA ↔ HDA is first-order like. The HDA phase is predominantly five-fold coordinated, whereas the LDA phase is the normal tetrahedrally bonded amorphous Si. The HDA phase at 400 K and 20 GPa was submitted to an isobaric annealing up to 800 K, resulting in a denser amorphous phase, which is structurally distinct from the HDA phase. Our results also show that the atomic volume and structure of this new amorphous phase are identical to those of the glass obtained by an isobaric quenching of the liquid in equilibrium at 2000 K and 20 GPa down to 400 K. The similarities between our results and those for amorphous ices suggest that this new phase is the very high density amorphous Si.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

An Atomistic Carbide-Derived Carbon Model Generated Using ReaxFF-Based Quenched Molecular Dynamics

We report a novel atomistic model of carbide-derived carbons (CDCs), which are nanoporous carbons with high specific surface areas, synthesis-dependent degrees of graphitization, and well-ordered, tunable porosities. These properties make CDCs viable substrates in several energy-relevant applications, such as gas storage media, electrochemical capacitors, and catalytic supports. These materials are heterogenous, non-ideal structures and include several important parameters that govern their performance. Therefore, a realistic model of the CDC structure is needed in order to study these systems and their nanoscale and macroscale properties with molecular simulation. We report the use of the ReaxFF reactive force field in a quenched molecular dynamics routine to generate atomistic CDC models. The pair distribution function, pore size distribution, and adsorptive properties of this model are reported and corroborated with experimental data. Simulations demonstrate that compressing the system after quenching changes the pore size distribution to better match the experimental target. Ring size distributions of this model demonstrate the prevalence of non-hexagonal carbon rings in CDCs. Furthermore, these effects may contrast the properties of CDCs against those of activated carbons with similar pore size distributions and explain higher energy densities of CDC-based supercapacitors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗