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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
DOE OSTI2015

Rock-salt structure lithium deuteride formation in liquid lithium with high-concentrations of deuterium: a first-principles molecular dynamics study

Because of lithium's possible use as a first wall material in a fusion reactor, a fundamental understanding of the interactions between liquid lithium (Li) and deuterium (D) is important. Here, we predict structural and dynamical properties of liquid Li samples with high concentrations of D, as derived from first-principles molecular dynamics simulations. Liquid Li samples with four concentrations of inserted D atoms (LiD$_{\beta}$ , $\beta =0.25$ , 0.50, 0.75, and 1.00) are studied at temperatures ranging from 470 to 1143 K. Densities, diffusivities, pair distribution functions, bond angle distribution functions, geometries, and charge transfer between Li and D atoms are calculated and analyzed. The analysis suggests liquid–solid phase transitions can occur at some concentrations and temperatures, forming rock-salt LiD within liquid Li. Finally, we observed the formation of some D 2 molecules at high D concentrations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Noise reduction in X-ray photon correlation spectroscopy with convolutional neural networks encoder–decoder models

Abstract Like other experimental techniques, X-ray photon correlation spectroscopy is subject to various kinds of noise. Random and correlated fluctuations and heterogeneities can be present in a two-time correlation function and obscure the information about the intrinsic dynamics of a sample. Simultaneously addressing the disparate origins of noise in the experimental data is challenging. We propose a computational approach for improving the signal-to-noise ratio in two-time correlation functions that is based on convolutional neural network encoder–decoder (CNN-ED) models. Such models extract features from an image via convolutional layers, project them to a low dimensional space and then reconstruct a clean image from this reduced representation via transposed convolutional layers. Not only are ED models a general tool for random noise removal, but their application to low signal-to-noise data can enhance the data’s quantitative usage since they are able to learn the functional form of the signal. We demonstrate that the CNN-ED models trained on real-world experimental data help to effectively extract equilibrium dynamics’ parameters from two-time correlation functions, containing statistical noise and dynamic heterogeneities. Strategies for optimizing the models’ performance and their applicability limits are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Conformational Dynamics on the Extracellular Side of LeuT Controlled by Na + and K + Ions and the Protonation State of Glu 290

Ions play key mechanistic roles in the gating dynamics of neurotransmitter:sodium symporters (NSSs). In other microsecond scale molecular dynamics simulations of a complete model of the dopamine transporter, a NSS protein, we observed a partitioning of K + ions from the intracellular side toward the unoccupied Na2 site of dopamine transporter following the release of the Na2-bound Na + . In this work, we evaluate with computational simulations and experimental measurements of ion affinities under corresponding conditions, the consequences of K+ binding in the Na2 site of LeuT, a bacterial homolog of NSS, when both Na + ions and substrate have left, and the transporter prepares for a new cycle. We compare the results with the consequences of binding Na + in the same apo system. Analysis of >50-μs atomistic molecular dynamics and enhanced sampling trajectories of constructs with Glu 290 , either charged or neutral, point to the Glu 290 protonation state as a main determinant in the structural reconfiguration of the extracellular vestibule of LeuT in which a “water gate” opens through coordinated motions of residues Leu 25 , Tyr 108 , and Phe 253 . The resulting water channel allows the binding/dissociation of the Na + and K + ions that are prevalent, respectively, in the extracellular and intracellular environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Nucleation and structural growth of cluster crystals

We study the nucleation of crystalline cluster phases in the generalized exponential model with exponent n = 4. Due to the finite value of this pair potential for zero separation, at high densities the system forms cluster crystals with multiply occupied lattice sites. Here, we investigate the microscopic mechanisms that lead to the formation of cluster crystals from a supercooled liquid in the low-temperature region of the phase diagram. Using molecular dynamics and umbrella sampling, we calculate the free energy as a function of the size of the largest crystalline nucleus in the system, and compare our results with predictions from classical nucleation theory. Employing bond-order parameters based on a Voronoi tessellation to distinguish different crystal structures, we analyze the average composition of crystalline nuclei. We find that even for conditions where a multiply occupied fcc crystal is the thermodynamically stable phase, the nucleation into bcc cluster crystals is strongly preferred. Furthermore, we study the particle mobility in the supercooled liquid and in the cluster crystal. In the cluster crystal, the motion of individual particles is captured by a simple reaction-diffusion model introduced previously to model the kinetics of hydrogen bonds.

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

Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation

Classical molecular dynamics simulates the time evolution of molecular systems through the phase space spanned by the positions and velocities of the constituent atoms. Molecular-level thermodynamic, kinetic, and structural data extracted from the resulting trajectories provide valuable information for the understanding, engineering, and design of biological and molecular materials. The cost of simulating many-body atomic systems makes simulations of large molecules prohibitively expensive, and the high-dimensionality of the resulting trajectories presents a challenge for analysis. Driven by advances in algorithms, hardware, and data availability, there has been a flare of interest in recent years in the applications of machine learning – especially deep learning – to molecular simulation. Furthermore, these techniques have demonstrated great power and flexibility in both extracting mechanistic understanding of the important nonlinear collective variables governing the dynamics of a molecular system, and in furnishing good low-dimensional system representations with which to perform enhanced sampling or develop long-timescale dynamical models. It is the purpose of this article to introduce the key machine learning approaches, describe how they are married with statistical mechanical theory into domain-specific tools, and detail applications of these approaches in understanding and accelerating biomolecular simulation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2016

Efficient molecular dynamics using geodesic integration and solvent–solute splitting

We present an approach to Langevin dynamics in the presence of holonomic constraints based on decomposition of the system into components representing geodesic flow, constrained impulse and constrained diffusion. We show that a particular ordering of the components results in an integrator that is an order of magnitude more accurate for configurational averages than existing alternatives. Moreover, by combining the geodesic integration method with a solvent–solute force splitting, we demonstrate that stepsizes of at least 8 fs can be used for solvated biomolecules with high sampling accuracy and without substantially altering diffusion rates, approximately increasing by a factor of two the efficiency of molecular dynamics sampling for such systems. The methods described in this article are easily implemented using the standard apparatus of modern simulation codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Application of the ESMACS Binding Free Energy Protocol to a Multi-Binding Site Lactate Dehydogenase A Ligand Dataset

Over the past two decades, the use of fragment-based lead generation has become a common, mature approach to identify tractable starting points in chemical space for the drug discovery process. This approach naturally involves the study of the binding properties of highly heterogeneous ligands. Such datasets challenge computational techniques to provide comparable binding free energy estimates from different binding modes. The performance of a range of statistically robust ensemble-based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent), is evaluated. Ligands designed to target two binding pockets in the lactate dehydogenase, a target protein, which vary in size, charge, and binding mode, are studied. When compared to experimental results, excellent statistical rankings are obtained across this highly diverse set of ligands. In addition, three approaches to account for entropic contributions are investigated: 1) normal mode analysis, 2) weighted solvent accessible surface area (WSAS), and 3) variational entropy. Normal mode analysis and WSAS correlate strongly with each other—although the latter is computationally far cheaper—but do not improve rankings. Variational entropy corrects exaggerated discrimination of ligands bound in different pockets but creates three outliers which reduce the quality of the overall ranking.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Comparing capacity coefficient and dual task assessment of visual multitasking workload

Capacity coefficient analysis could offer a theoretically grounded alternative approach to subjective measures and dual task assessment of cognitive workload. Workload capacity or workload efficiency is a human information processing modeling construct defined as the amount of information that can be processed by the visual cognitive system given a specified of amount of time. In this paper, I explore the relationship between capacity coefficient analysis of workload efficiency and dual task response time measures. To capture multitasking performance, I examine how the relatively simple assumptions underlying the capacity construct generalize beyond the single visual decision making tasks. The fundamental tools for measuring workload efficiency are the integrated hazard and reverse hazard functions of response times, which are defined by log transforms of the response time distribution. These functions are used in the capacity coefficient analysis to provide a functional assessment of the amount of work completed by the cognitive system over the entire range of response times. For the study of visual multitasking, capacity coefficient analysis enables a comparison of visual information throughput as the number of tasks increases from one to two to any number of simultaneous tasks. I illustrate the use of capacity coefficients for visual multitasking on sample data from dynamic multitasking in the modified Multi-attribute Task Battery.

capacity coefficient↗
DOE OSTI2022

Assessing entropy for catalytic processes at complex reactive interfaces

When chemical reactions are accelerated by a catalyst, entropy differences between reactants and their transient intermediates can be the driving force behind the promotion or inhibition of desired and parasitic chemical pathways. Understanding and controlling catalytic processes therefore requires both a fundamental and practicable understanding of entropy in addition to enthalpy. In unstructured media such as the vapor phase equilibrated with sparsely covered surfaces, entropy can be adequately accounted for by well-established approaches based on translational, rotational, and harmonic vibrational partition functions. However, these approximations become inadequate in more complex condensed phase environments, e.g., solid liquid interfaces of confined reaction spaces. In this chapter, we provide an overview of the state-of-art in the computational quantification of entropy and its known ramifications on catalysis. The fundamental roles of thermodynamics and kinetics in catalysis are covered in enough detail to appreciate and contextualize the computational methods employed to compute chemically accurate estimates of entropy. These methods are discussed in appropriate detail and range from the ubiquitous harmonic oscillator approximation where entropy unrelated to high frequency oscillations is typically underestimated, to enhanced free energy sampling with molecular dynamics where the desired accuracy must be weighed against the associated computational cost of obtaining it. The rising importance of machine learning and artificial intelligence in accelerating methodological progress in this field is touched upon, as well. Finally, applications, successes, and pitfalls of using these methods are provided to showcase past and present accomplishments while clarifying where improvements in both understanding and methodology are still needed.

Kollias, Loukas↗
DOE OSTI2016

Computational approaches to detect allosteric pathways in transmembrane molecular machines

Many of the functions of transmembrane proteins involved in signal processing and transduction across the cell membrane are determined by allosteric couplings that propagate the functional effects well beyond the original site of activation. Data gathered from breakthroughs in biochemistry, crystallography, and single molecule fluorescence have established a rich basis of information for the study of molecular mechanisms in the allosteric couplings of such transmembrane proteins. The mechanistic details of these couplings, many of which have therapeutic implications, however, have only become accessible in synergy with molecular modeling and simulations. Here, we review some recent computational approaches that analyze allosteric coupling networks (ACNs) in transmembrane proteins, and in particular the recently developed Protein Interaction Analyzer (PIA) designed to study ACNs in the structural ensembles sampled by molecular dynamics simulations. The power of these computational approaches in interrogating the functional mechanisms of transmembrane proteins is illustrated with selected examples of recent experimental and computational studies pursued synergistically in the investigation of secondary active transporters and GPCRs. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov.

Biochemistry & Molecular Biology↗
DOE OSTI2019

phq: A Fortran code to compute phonon quasiparticle properties and dispersions

We here introduce a Fortran code that computes anharmonic free energy of solids from first-principles based on our phonon quasiparticle approach. In this code, phonon quasiparticle properties, i.e., renormalized phonon frequencies and lifetimes, are extracted from mode-projected velocity auto-correlation functions (VAF) of modes sampled by molecular dynamics (MD) trajectories. Using renormalized frequencies as input, the code next constructs an effective harmonic force constant matrix to calculate anharmonic phonon dispersions over the whole Brillouin zone and thus the anharmonic free energy in the thermodynamic limit ( N → ∞ ). A detailed description of all the input parameters and the subroutines is provided as well. We illustrate the use of this code to compute ab initio temperature-dependent anharmonic phonons of Si in the diamond structure.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

The exit-wave power-cepstrum transform for scanning nanobeam electron diffraction: robust strain mapping at subnanometer resolution and subpicometer precision

Scanning nanobeam electron diffraction (NBED) with fast pixelated detectors is a valuable technique for rapid, spatially resolved mapping of lattice structure over a wide range of length scales. However, intensity variations caused by dynamical diffraction and sample mistilts can hinder the measurement of diffracted disk centers as necessary for quantification. Robust data processing techniques are needed to provide accurate and precise measurements for complex samples and non-ideal conditions. Here we present an approach to address these challenges using a transform, called the exit wave power cepstrum (EWPC), inspired by cepstral analysis in audio signal processing. The EWPC transforms NBED patterns into real-space patterns with sharp peaks corresponding to inter-atomic spacings. We describe a simple analytical model for interpretation of these patterns that cleanly decouples lattice information from the intensity variations in NBED patterns caused by tilt and thickness. With tracking the inter-atomic spacing peaks in EWPC patterns, strain mapping is demonstrated for two practical applications: mapping of ferroelectric domains in epitaxially strained PbTiO 3 films and mapping of strain profiles in arbitrarily oriented core-shell Pt-Co nanoparticle fuel-cell catalysts. The EWPC transform enables lattice structure measurement at sub-pm precision and sub-nm resolution that is robust to small sample mistilts and random orientations.

42 ENGINEERING↗
DOE OSTI2021

Single-Atom Metal Oxide Sites as Traps for Charge Separation in the Zirconium-Based Metal–Organic Framework NDC–NU-1000

Solvothermal deposition in metal–organic frameworks (MOFs) can be utilized to mount single-metal-atom catalytic species at chemically reactive sites on hexa-Zr(IV)-oxy(oxo,hydroxo,aqua) nodes in nanoscale crystallites of the MOF NDC-NU-1000 in a self-limiting fashion. Upon photoexcitation of the 1,3,6,8-tetrakis( p -benzoato)pyrene chromophores of the parent framework, charge transfer may occur between the chromophores and the installed heterometal sites. Extended X-ray absorption fine structure studies revealed the single-atom nature of the installed species. A combination of steady-state and ultrafast optical spectroscopy was utilized to uncover evidence of a charge-separated (CS) state arising in the metalated samples. The relevant dynamics were characterized with transient photoluminescence and femtosecond transient absorption spectroscopy. Here, we find that a titanium-oxy single atom site gives rise to the longest-lived CS species compared to cobalt and nickel in a similar arrangement. This study provides guidance in designing MOF-based catalytic systems for photocatalysis and solar-fuel production.

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

Data-Driven Discovery of Linear Molecular Probes with Optimal Selective Affinity for PFAS in Water

Approaches to tackle the wide and growing variety of highly persistent per- and polyfluoroalkyl substances (PFAS) are of pressing global need because of their detrimental human health effects, such as cancer, birth defects, and hormone imbalance. Sensitive, selective, and easy-to-use real-time sensors to monitor and detect PFAS and sorbents to extract them are critical to meeting government-mandated environmental concentrations. In this work, we combine all-atom molecular dynamics simulations, enhanced sampling, deep representational learning, and Bayesian optimization to perform high-throughput virtual screening for highly sensitive and selective molecular probes. Our molecular design space consists of 3850 linear hydrocarbon chains with varying degrees of halogenation with and without amine- and phosphine-based headgroups. By employing a data-driven search process, we efficiently explore the molecular design space to optimize the sensitivity to perfluorooctanesulfonic acid (PFOS) as a prototypical PFAS analyte and selectivity relative to a sodium dodecyl sulfate (SDS) interferent. We calculate 504 Gibbs free energies of probe-analyte and probe-interferent interactions and identify probes with PFOS association free energies of up to (-ΔG PFOS ) = 9.8 ± 0.2 kJ/mol and selectivities relative to SDS of (-ΔΔG PFOS–SDS ) = 3.1 ± 1.5 kJ/mol. A C 11 Br 23 P(CH 3 ) 2 probe containing 11 backbone brominated carbons and a tertiary phosphine headgroup possesses the most sensitive binding constant to PFOS within the defined search space of K b PFOS = 177.4 ± 12.7, and a semibrominated probe C 5 H 11 C 7 Br 14 N(CH 3 ) 2 containing 12 backbone carbons and a tertiary amine headgroup possesses the highest selectivity relative to SDS of K b PFOS /K b SDS = 4.6 ± 1.7. A retrospective analysis of our data to extract interpretable design rules reveals that the sensitivity of linear hydrogenated probes increases by approximately 1 kJ/mol per C–C bond. The addition or removal of halogen atoms and amine or phosphine headgroups produces nonmonotonic changes in both sensitivity and selectivity with changes to the sensitivity of up to 2.5 kJ/mol. Finally, this work places empirical limitations on the performance of a wide range of linear probes for PFOS detection and offers a generic strategy for high-throughput computational screening to promote selective and sensitive binding.

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

Ion Dissociation Dynamics in an Aqueous Premelting Layer

Using molecular dynamics simulations and methods of importance sampling, we study the thermodynamics and dynamics of sodium chloride in the aqueous premelting layer formed spontaneously at the interface between ice and its vapor. We uncover a hierarchy of time scales that characterize the relaxation dynamics of this system, spanning the picoseconds of ionic motion to the tens or hundreds of nanoseconds associated with fluctuations of the liquid-crystal interface in their presence. We find that ions distort both local interfaces, incurring restoring forces that result in the ions preferentially residing in the middle of the layer. While ion pair dissociation is thermodynamically favorable, these structural and dynamic effects cause its rate to vary by over an order of magnitude through the layer, with a maximum rate significantly depressed from the corresponding bulk value. The solvation environment of ions in the premelting layer is distinct from that in a bulk liquid, being dominated by slow reorganization of water molecules and a water structure intermediate between ice and its melt.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Molecular Simulations of the Hydration Behavior of a Zwitterion Brush Array and Its Antifouling Property in an Aqueous Environment

We carried out umbrella sampling and molecular dynamics (MD) simulations to investigate molecular interactions between sulfobetaine zwitterions or between sulfobetaine brushes in different media. Simulation results show that it is more energetically favorable for the two sulfobetaine zwitterions or brushes to be fully hydrated in aqueous solutions than in vacuum where strong ion pairs are formed. Structural properties of the hydrated sulfobetaine brush array and its antifouling behavior against a foulant gel are subsequently studied through steered MD simulations. We find that sulfobetaine brush arrays with different grafting densities have different structures and antifouling mechanisms. At a comparably higher grafting density, the sulfobetaine brush array exhibits a more organized structure which can hold a tightly bound hydration water layer at the interface. Compression of this hydration layer results in a strong repulsive force. However, at a comparably lower grafting density, the brush array exhibits a randomly oriented structure in which the antifouling of the brush array is through the deformation of the sulfobetaine branches.

36 MATERIALS SCIENCE↗