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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Hydrogen Bond Network Disruption by Hydration Layers in Water Solutions with Salt and Hydrogen-Bonding Polymers (PEO)

A mean field theory model describing the interaction of ion hydration layers with the network of hydrogen bonds of both water and the nonionic polymer poly(ethylene oxide) (PEO) is presented. The predictions of the model for types and statistics of hydrogen bonds, the number of water molecules bound to PEO, or their dependence on temperature are successfully verified from all-atom simulations at different NaCl and PEO concentrations. Furthermore, our simulations show that the binding of cations to PEO increases monotonically with salt concentration, in agreement with recent experimental results, through a mechanism in which the sum of the number of bound water and cations is independent of salt concentration. Here, the model introduced is general and can describe any salt or hydrogen-bond-forming polymer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The Potential Energy Hotspot: Effects of Impact Velocity, Defect Geometry, and Crystallographic Orientation

In energetic materials, the localization of energy into “hotspots” is known to dictate the initiation of chemical reactions and detonation. Recent all-atom simulations have shown that more energy is localized as internal potential energy (PE) than can be inferred from the kinetic energy (KE) alone. The mechanisms associated with pore collapse and hotspot formation are known to depend on pore geometry and dynamic material response such as plasticity. Therefore, we use molecular dynamics (MD) simulations to characterize shock-induced pore collapse and the subsequent formation of hotspots in 1,3,5-triamino-2,4,6-trinitrobenzene (TATB), a highly anisotropic molecular crystal, for various defect shapes, shock strengths, and crystallographic orientations. We find that the localization of energy as PE is consistently larger than the KE in cases with significant plastic deformation. Additionally, an analysis of MD trajectories reveals the underlying molecular- and crystal-level processes that govern the effect of orientation and pore shape on PE localization. We find that the regions of highest PE relate to the areas of maximum plastic deformation, while KE is maximized at the point of impact. Comparisons against octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) reveal less energy localization in TATB, which could be a contributing factor to the latter’s insensitivity.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Uncertainty quantification for misspecified machine learned interatomic potentials

The use of high-dimensional regression techniques from machine learning has significantly improved the quantitative accuracy of interatomic potentials. Atomic simulations can now plausibly target quantitative predictions in a variety of settings, which has brought renewed interest in robust means to quantify uncertainties. In many practical settings where model complexity is constrained (e.g., due to performance considerations), misspecification — the inability of any one choice of model parameters to exactly match all training data — is a key contributor to errors that is often disregarded. Here, we employ a recent misspecification-aware regression technique to quantify parameter uncertainties, which is then propagated to a broad range of phase and defect properties in tungsten. The propagation is performed through both brute-force resampling and implicit Taylor expansion. The propagated misspecification uncertainties robustly quantify and bound errors on a broad range of material properties. We demonstrate application to recent foundational machine learning interatomic potentials, accurately predicting and bounding errors in MACE-MPA-0 energy predictions across the diverse materials project database.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Metastable doubly threaded [3]rotaxanes with a large macrocycle

Ring size is a critically important parameter in many interlocked molecules as it directly impacts many of the unique molecular motions that they exhibit. Reported herein are studies using one of the largest macrocycles reported to date to synthesize doubly threaded [3]rotaxanes. A large ditopic 46 atom macrocycle containing two 2,6-bis(N-alkyl-benzimidazolyl)pyridine ligands has been used to synthesize several metastable doubly threaded [3]rotaxanes in high yield (65–75% isolated) via metal templating. Macrocycle and linear thread components were synthesized and self-assembled upon addition of iron(II) ions to form the doubly threaded pseudo[3]rotaxanes that could be subsequently stoppered using azide–alkyne cycloaddition chemistry. Following demetallation with base, these doubly threaded [3]rotaxanes were fully characterized utilizing a variety of NMR spectroscopy, mass spectrometry, size-exclusion chromatography, and all-atom simulation techniques. Critical to the success of accessing a metastable [3]rotaxane with such a large macrocycle was the nature of the stopper group employed. By varying the size of the stopper group it was possible to access metastable [3]rotaxanes with stabilities in deuterated chloroform ranging from a half-life of <1 minute to ca. 6 months at room temperature potentially opening the door to interlocked materials with controllable degradation rates.

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

GPAW: An open Python package for electronic structure calculations

We review the GPAW open-source Python package for electronic structure calculations. GPAW is based on the projector-augmented wave method and can solve the self-consistent density functional theory (DFT) equations using three different wave-function representations, namely real-space grids, plane waves, and numerical atomic orbitals. The three representations are complementary and mutually independent and can be connected by transformations via the real-space grid. This multi-basis feature renders GPAW highly versatile and unique among similar codes. By virtue of its modular structure, the GPAW code constitutes an ideal platform for the implementation of new features and methodologies. Moreover, it is well integrated with the Atomic Simulation Environment (ASE), providing a flexible and dynamic user interface. In addition to ground-state DFT calculations, GPAW supports many-body GW band structures, optical excitations from the Bethe–Salpeter Equation, variational calculations of excited states in molecules and solids via direct optimization, and real-time propagation of the Kohn–Sham equations within time-dependent DFT. A range of more advanced methods to describe magnetic excitations and non-collinear magnetism in solids are also now available. In addition, GPAW can calculate non-linear optical tensors of solids, charged crystal point defects, and much more. Recently, support for graphics processing unit (GPU) acceleration has been achieved with minor modifications to the GPAW code thanks to the CuPy library. We end the review with an outlook, describing some future plans for GPAW.

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

Identifying sequence perturbations to an intrinsically disordered protein that determine its phase-separation behavior

Phase separation of intrinsically disordered proteins (IDPs) commonly underlies the formation of membraneless organelles, which compartmentalize molecules intracellularly in the absence of a lipid membrane. Identifying the protein sequence features responsible for IDP phase separation is critical for understanding physiological roles and pathological consequences of biomolecular condensation, as well as for harnessing phase separation for applications in bioinspired materials design. To expand our knowledge of sequence determinants of IDP phase separation, we characterized variants of the intrinsically disordered RGG domain from LAF-1, a model protein involved in phase separation and a key component of P granules. Based on a predictive coarse-grained IDP model, we identified a region of the RGG domain that has high contact probability and is highly conserved between species; deletion of this region significantly disrupts phase separation in vitro and in vivo. We determined the effects of charge patterning on phase behavior through sequence shuffling. We designed sequences with significantly increased phase separation propensity by shuffling the wild-type sequence, which contains well-mixed charged residues, to increase charge segregation. This result indicates the natural sequence is under negative selection to moderate this mode of interaction. We measured the contributions of tyrosine and arginine residues to phase separation experimentally through mutagenesis studies and computationally through direct interrogation of different modes of interaction using all-atom simulations. Finally, we show that despite these sequence perturbations, the RGG-derived condensates remain liquid-like. Together, these studies advance our fundamental understanding of key biophysical principles and sequence features important to phase separation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The SARS-CoV-2 Spike Variant D614G Favors an Open Conformational State

The COVID-19 pandemic underwent a rapid transition with the emergence of a dominant viral variant (from the “D-form” to the “G-form”) that carried an amino acid substitution D614G in its “Spike” protein. The G-form is more infectious in vitro and associated with increased viral loads. To gain insight into the molecular-level underpinnings of these characteristics, we employed microsecond all-atom simulations. Here we show that changes in the protein energetics favor a higher population of infection-capable (open) states through release of hydrogen bonds of an asymmetry present in the D-form but not the G-form. Thus, the increased infectivity of the G-form is likely due to a higher rate of profitable binding encounters with the host receptor. It is also predicted to be more neutralization sensitive due to enhanced exposure of the receptor binding domain, a key target region for neutralizing antibodies. These results are significant for vaccine design. The Molecular Dynamics Simulations datasets generated in this study, namely that of the soluble form SARS-CoV-2 Spike protein are made available here. Details of the trajectory and supporting files are given in the README.txt file.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Mechanism of Peptide Agonist Binding in CXCR4 Chemokine Receptor

Chemokine receptors are key G-protein-coupled receptors (GPCRs) that control cell migration in immune system responses, development of cardiovascular and central nervous systems, and numerous diseases. In particular, the CXCR4 chemokine receptor promotes metastasis, tumor growth and angiogenesis in cancers. CXCR4 is also used as one of the two co-receptors for T-tropic HIV-1 entry into host cells. Therefore, CXCR4 serves as an important therapeutic target for treating cancers and HIV infection. Apart from the CXCL12 endogenous peptide agonist, previous studies suggested that the first 17 amino acids of CXCL12 are sufficient to activate CXCR4. Two 17-residue peptides with positions 1–4 mutated to RSVM and ASLW functioned as super and partial agonists of CXCR4, respectively. However, the mechanism of peptide agonist binding in CXCR4 remains unclear. Here, we have investigated this mechanism through all-atom simulations using a novel Peptide Gaussian accelerated molecular dynamics (Pep-GaMD) method. The Pep-GaMD simulations have allowed us to explore representative binding conformations of each peptide and identify critical low-energy states of CXCR4 activated by the super versus partial peptide agonists. Our simulations have provided important mechanistic insights into peptide agonist binding in CXCR4, which are expected to facilitate rational design of new peptide modulators of CXCR4 and other chemokine receptors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Exploring the Role of Glycans in the Interaction of SARS-CoV-2 RBD and Human Receptor ACE2

COVID-19 is a highly infectious respiratory disease caused by the novel coronavirus SARS-CoV-2. It has become a global pandemic and its frequent mutations may pose new challenges for vaccine design. During viral infection, the Spike RBD of SARS-CoV-2 binds the human host cell receptor ACE2, enabling the virus to enter the host cell. Both the Spike and ACE2 are densely glycosylated, and it is unclear how distinctive glycan types may modulate the interaction of RBD and ACE2. Detailed understanding of these determinants is key for the development of novel therapeutic strategies. To this end, we perform extensive all-atom simulations of the (i) RBD-ACE2 complex without glycans, (ii) RBD-ACE2 with oligomannose MAN9 glycans in ACE2, and (iii) RBD-ACE2 with complex FA2 glycans in ACE2. These simulations identify the key residues at the RBD-ACE2 interface that form contacts with higher probabilities, thus providing a quantitative evaluation that complements recent structural studies. Notably, we find that this RBD-ACE2 contact signature is not altered by the presence of different glycoforms, suggesting that RBD-ACE2 interaction is robust. Applying our simulated results, we illustrate how the recently prevalent N501Y mutation may alter specific interactions with host ACE2 that facilitate the virus-host binding. Furthermore, our simulations reveal how the glycan on Asn90 of ACE2 can play a distinct role in the binding and unbinding of RBD. Finally, an energetics analysis shows that MAN9 glycans on ACE2 decrease RBD-ACE2 affinity, while FA2 glycans lead to enhanced binding of the complex. Together, our results provide a more comprehensive picture of the detailed interplay between virus and human receptor, which is much needed for the discovery of effective treatments that aim at modulating the physical-chemical properties of this virus.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Atomic structures for studying elemental segregation at garnet grain boundaries

This is a dataset containing atomic structures of bulk and grain boundaries of doped/undoped Li7La3Zr2O12 solid electrolytes, which were obtained from atomic simulations in a journal paper "Elemental Segregation at Garnet Grain Boundaries: Eliminating Its Detrimental Role in Conductivity and Lithium Nucleation" by Kai Yao et al.

Kim, Kwangnam [Lawrence Livermore National Laborat↗
NASA NTRS1984

Atomic beam system for laboratory simulation of upper atmosphere oxygen atom impact on STS surfaces

An atomic beam apparatus capable of producing a collimated beam of O atoms or other atmospheric species at the STS impact velocity of 8 km/s is described. A conventional ion beam charge transfer neutralization approach was adopted to produce the projectile beam. The prime considerations favoring this approach are: the ability to vary the velocity of the impacting species at will over a wide range on either side of the optimum required value of 8 km/s; and sample irradiation can be made with species other than O-atoms, such as O2, N, or N2.

Mahadevan, P.↗
DOE OSTI2025

Linear Discriminant Analysis-Based Machine Learning and All-Atom Molecular Dynamics Simulations for Probing Electro-Osmotic Transport in Cationic-Polyelectrolyte-Brush-Grafted Nanochannels

Deciphering the correct mechanisms governing certain phenomena in polyelectrolyte (PE) brush grafted systems, revealed through atomistic simulations, is an extremely challenging problem. In a recent study, our all-atom molecular dynamics (MD) simulations revealed a non-linearly large electroosmotic (EOS) flow (in the presence of an applied electric field) in nanochannels grafted with PMETAC [Poly(2-(methacryloyloxy)ethyl trimethylammonium chloride] brushes. Given the lack of any formal procedure that would have directed us to identify the correct factors responsible for such an occurrence, we needed to spend several months and devote significant analyses to unravel the involved mechanisms. In this paper, we propose a Linear Discriminant Analysis (LDA) based Machine Learning (ML) approach to address this gap. At first, we obtain data on certain basic features from the all-atom MD data. These basic features represent the number of atoms of certain species around one atom of another (or same) species. Here, we obtain such data on basic features for a reference case (case of an EOS flow in PMETAC-brush-grafted nanochannels with a smaller electric field) and a perturbed case (case of an EOS flow in PMETAC-brush-grafted nanochannels with a larger electric field) in bins in which the nanochannel half height has been divided into. These datasets are high-dimensional dataset, to which the LDA is applied. This leads to the projection of the data (between the reference and the perturbed states) in a highly separated form on a 1D line. From such LDA calculations, we are able to identify the relative importance of the different basic features in ensuring this separation of the data (between the reference and the perturbed states) on the 1D line. This relative importance of the different basic features is quantified as “importance scores” for the different features, which in turn tell us what to study and where to study. Such knowledge enables us to rapidly identify the key factors responsible for the non-linearly large EOS transport in PMETAC-brush-grafted nanochannels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Machine learning enabled quantification of the hydrogen bonds inside the polyelectrolyte brush layer probed using all-atom molecular dynamics simulations

The configuration of densely grafted charged polyelectrolyte (PE) brushes is strongly dictated by the properties and behavior of the counterions that screen the PE brush charges and the solvent molecules (typically water) that solvate the brush molecules and these screening counterions. Only recently, efforts have been made to study the PE brushes atomistically, thereby shedding light on the properties of brush-supported ions and water molecules. However, even for such efforts, there are limitations associated with using a generic definition to estimate certain properties of water and ions inside the brush layer. For example, water–water hydrogen bonds (HBs) will behave differently for locations outside and inside the brush layer, given the fact that the densely closely grafted PE brush molecules create a soft nanoconfinement where the water connectivity becomes highly disrupted: therefore, using the same definition to quantify the HBs inside and outside the brush layer will be unwise. In this paper, we address this limitation by employing an unsupervised machine learning (ML) approach to predict the water–water hydrogen bonding inside a cationic PE brush layer modeled using all-atom molecular dynamics (MD) simulations. Here, the ML method, which relies on a clustering approach and uses the equilibrium coordinates of the water molecules (obtained from the all-atom MD simulations) as the input, is capable of identifying the structural modification of water–water HBs (revealed through appropriate clustering of the data) inside the PE brush layer induced soft nanoconfinement. Such capabilities would not have been possible by using a generic definition of the HBs. Our calculations lead to four key findings: (1) the clusters formed inside and outside the brush layer are structurally similar; (2) the margin of the cluster is shorter inside the PE brush layer confirming the possible disruption of the HBs inside the PE brush layer; (3) the average “hydrogen–acceptor-oxygen–donor-oxygen” angle that defines the HB is reduced for the HBs formed inside the brush layer; (4) the use of the generic definition (definition usable for characterizing the HBs in brush-free bulk) leads to an overprediction of the number of HBs formed inside the PE brush layer.

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

All-atom molecular dynamics simulations of weak polyionic brushes: influence of charge density on the properties of polyelectrolyte chains, brush-supported counterions, and water molecules

All atom molecular dynamics (MD) simulations of planar Na+-counterion-neutralized polyacrylic acid (PAA) brushes are performed for varying degrees of ionization (and thereby varying charge density) and varying grafting density. Variation in the PE charge density (or degree of ionization) and grafting density leads to massive changes of the properties of the PE molecules (quantified by the changes in the height and the mobility of the PE brushes) as well as the local arrangement and distribution of the brush-supported counterions and water molecules within the brushes. The effect on the counterions is manifested by the corresponding variation of the counterion mobility, counterion concentration, extent of counterion binding to the charged site of the PE brushes, water-in-salt-like structure formation, and counterion-water–oxygen radial distribution function within the PE brushes. On the other hand, the effect on water molecules is manifested by the corresponding variation of water–oxygen–water–oxygen RDF, local water density, water–water and water–PE functional group hydrogen bond networks, static dielectric constant of water molecules, orientational tetrahedral order parameter, and water mobility. Enforcing such varying degree of ionization of weak polyelectrolytes is possible by changing the pH of the surrounding medium. Thus, our results provide insights into the changes in microstructure (at the atomistic level) of weak polyionic brushes at varying pH. Finally, we anticipate that this knowledge will prove to be vital for the efficient design of several nano-scale systems employing PE brushes such as nanomechanical gates, current rectifiers, etc.

42 ENGINEERING↗
DOE OSTI2024

All-Atom Molecular Dynamics Simulations of Cationic Polyelectrolyte Brushes in the Presence of Halide Counterions

Understanding the response of the charged polyelectrolyte (PE) brushes and brush-supported water and ions to the changes in the nature of screening counterions is significant in developing strategies for utilizing PE brushes in various applications. In this paper, we employ all-atom molecular dynamics (MD) simulations for studying the behavior of cationic [poly(2-(methacryloyloxy)ethyl) trimethylammonium] (PMETA) brushes and brush-supported water and ions in the presence of different halide (X: I – , Br – , Cl – , and F – ) screening counterions. We find that despite the F– ion having the largest charge density, the extent of binding of the counterions on the PMETAX brushes varies as I – > Br – > Cl – > F – , leading to PMETAX brush height being least with I– counterions and greatest with F – counterions. This trend in the binding of the halide ions matches the previous experimental result and can be explained by identifying the chaotropic nature of the I – and Br – ions that promote a disruption of water structure and a more favorable binding of the ions to the polymer chains. Such a binding trend also ensures that the order of the water molecules around the PMETAX chains or the counterions as well as the number of water–water hydrogen bonds inside the brush layer increases in the following order of the counterion-specific PMETAX brushes: F – > Cl – > Br – > I – . Furthermore, halide-ion-PMETAX-chain binding takes place via both interchain and intrachain bridging: intrachain bridging dominates for the case of counterions that show enhanced binding (I – and Br – ), while interchain bridging is more favored for the case of counterions that show weakened binding (F – and Cl – ). Also, a greater degree of intrachain bridging leads to greater compressibility and flexibility of the brush layer. Lastly, we show that the mobility of the halide ions follows a nonmonotonic trend with the charge density: the mobility decreases as I – < Br – < Cl – (as their binding to the PMETAX chains varies as I – > Br – > Cl – ), but the mobility of F – ions is in between that of I – and Br – ions. We argue that the strongly attached hydration layer and the ensuing friction inside the brush layer lead to such a reduced mobility of the F – ions.

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

Benchmarking highly entangled states on a 60-atom analogue quantum simulator

Abstract Quantum systems have entered a competitive regime in which classical computers must make approximations to represent highly entangled quantum states 1,2 . However, in this beyond-classically-exact regime, fidelity comparisons between quantum and classical systems have so far been limited to digital quantum devices 2–5 , and it remains unsolved how to estimate the actual entanglement content of experiments 6 . Here, we perform fidelity benchmarking and mixed-state entanglement estimation with a 60-atom analogue Rydberg quantum simulator, reaching a high-entanglement entropy regime in which exact classical simulation becomes impractical. Our benchmarking protocol involves extrapolation from comparisons against an approximate classical algorithm, introduced here, with varying entanglement limits. We then develop and demonstrate an estimator of the experimental mixed-state entanglement 6 , finding our experiment is competitive with state-of-the-art digital quantum devices performing random circuit evolution 2–5 . Finally, we compare the experimental fidelity against that achieved by various approximate classical algorithms, and find that only the algorithm we introduce is able to keep pace with the experiment on the classical hardware we use. Our results enable a new model for evaluating the ability of both analogue and digital quantum devices to generate entanglement in the beyond-classically-exact regime, and highlight the evolving divide between quantum and classical systems.

Science & Technology - Other Topics↗