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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Toward direct spatial and intensity characterization of ultra-high-intensity laser pulses using ponderomotive scattering of free electrons

Spatial distributions of electrons ionized and scattered from ultra-low-pressure gases are proposed and experimentally demonstrated as a method to directly measure the intensity of an ultra-high-intensity laser pulse. Analytic models relating the peak scattered electron energy to the peak laser intensity are derived and compared to paraxial Runge–Kutta simulations highlighting two models suitable for describing electrons scattered from weakly paraxial beams (f#>5) for intensities in the range of 1018−1021 W cm−2. Scattering energies are shown to be dependent on gas species, emphasizing the need for specific gases for given intensity ranges. Direct measurements of the laser intensity at full power of two laser systems are demonstrated, both showing a good agreement between indirect methods of intensity measurement and the proposed method. One experiment exhibited the role of spatial aberrations in the scattered electron distribution, motivating a qualitative study on the effect. We propose the use of convolutional neural networks as a method for extracting quantitative information on the spatial structure of the laser at full power. We believe the presented technique to be a powerful tool that can be immediately implemented in many high-power laser facilities worldwide.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

A multilayer network analysis of Alzheimer's disease pathogenesis: Roles for p‐tau, synaptic peptides, and physical activity

INTRODUCTION: In the aging brain, cognitive abilities emerge from the coordination of complex pathways arising from a balance between protective lifestyle and environmental factors and accumulation of neuropathologies. METHODS: As part of the Rush Memory and Aging Project (n = 440), we measured accelerometer-based actigraphy, cognitive performance, and after brain autopsy, selected reaction monitoring mass spectrometry. Multilevel network analysis was used to examine the relationships among the molecular machinery of vesicular neurotransmission, Alzheimer's disease (AD) neuropathology, cognition, and late-life physical activity. RESULTS: Synaptic peptides involved in neuronal secretory function were the most influential contributors to the multilayer network, reflecting the complex interdependencies among AD pathology, synaptic processes, and late-life cognition. Older adults with lower physical activity evidenced stronger adverse relationships among phosphorylated tau peptides, markers of synaptic integrity, and tangle pathology. DISCUSSION: Network-based approaches simultaneously model interdependent biological processes and advance understanding of the role of physical activity in age-associated cognitive impairment. Highlights: Network-based approaches simultaneously model interdependent biological processes. Secretory synaptic peptides were influential contributors to the multilayer network. Older adults with lower physical activity had adverse relationships among pathology. There was interdependence among phosphorylated tau, synaptic integrity, and tangles. Network methods elucidate the role of physical activity in cognitive impairment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

The Rise of Neural Networks for Materials and Chemical Dynamics

Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. ML-based force fields, trained on large data sets of high-quality electron structure calculations, are particularly attractive due their unique combination of computational efficiency and physical accuracy. This Perspective summarizes some recent advances in the development of neural network-based interatomic potentials. Designing high-quality training data sets is crucial to overall model accuracy. One strategy is active learning, in which new data are automatically collected for atomic configurations that produce large ML uncertainties. Another strategy is to use the highest levels of quantum theory possible. Transfer learning allows training to a data set of mixed fidelity. A model initially trained to a large data set of density functional theory calculations can be significantly improved by retraining to a relatively small data set of expensive coupled cluster theory calculations. These advances are exemplified by applications to molecules and materials.

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

When do short-range atomistic machine-learning models fall short?

In this work, we explore the role of long-range interactions in atomistic machine-learning models by analyzing the effects on fitting accuracy, isolated cluster properties, and bulk thermodynamic properties. Such models have become increasingly popular in molecular simulations given their ability to learn highly complex and multi-dimensional interactions within a local environment; however, many of them fundamentally lack a description of explicit long-range interactions. In order to provide a well-defined benchmark system with precisely known pairwise interactions, we chose as the reference model a flexible version of the Extended Simple Point Charge (SPC/E) water model. Our analysis shows that while local representations are sufficient for predictions of the condensed liquid phase, the short-range nature of machine-learning models falls short in representing cluster and vapor phase properties. These findings provide an improved understanding of the role of long-range interactions in machine learning models and the regimes where they are necessary.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Conformational Isomerization of Imide Anions Governs Solvation and Transport in Water-in-Salt Electrolytes

The behavior of highly concentrated electrolytes departs radically from the dilute-solution theory, yet the molecular origin of this transformation remains unresolved. Here, we identify the conformational isomerization of molecular ions as a decisive, previously unrecognized control parameter governing structure and transport in crowded aqueous electrolytes. Across a series of fluorosulfonimide anions, we show that increasing concentration drives a collective shift from extended transoid to compact cisoid conformers, revealed by small-angle X-ray scattering, vibrational spectroscopy, pulsed-field gradient NMR, and molecular dynamics simulations. This conformational transition triggers a collapse of the hydrogen-bonded water network and the emergence of densely packed ionic domains with confined water, producing a qualitative change in Li+ transport from solvent-mediated diffusion to network-confined hopping. Anion size and asymmetry systematically tune the onset of this transition, demonstrating that molecular geometry dictates mesoscale organization and dynamics in the ion-rich regime. Our results establish ion conformation, not merely composition or coordination, as a fundamental thermodynamic variable in concentrated solutions, providing a chemical framework that unifies solvation structure and transport in water-in-salt electrolytes and suggesting new principles for designing dense ionic media.

Nguyen, Huong TD↗
DOE OSTI2025

Effect of Ambient Organic Acids on the Water Structure at ${\rm TiO}_{2}$ Interfaces

A molecular-level understanding of the effects of ambient organic compounds on the wettability of titanium dioxide ( ${\rm TiO}_{2}$ ) surfaces is relevant to many of its energy-related and environmental applications. Herein, we focus on two common atmospheric carboxylic acids, formic and acetic acid, and characterize their adsorption/ desorption at the aqueous interfaces of anatase and rutile ${\rm TiO}_{2}$ using molecular dynamics with an ab initio deep neural network potential. Our simulations show that these acids prefer to be localized in the interfacial water layers close to the ${\rm TiO}_{2}$ surface where they are stabilized by the interaction/exchange of their acid proton with a surface oxygen, rather than chemisorb at the surface Ti sites by displacing the adsorbed water. Notably, these acids make the surface of anatase hydrophobic, whereas the larger fraction of adsorbed water dissociation can offset their effect on rutile. Furthermore, these results provide a picture where carboxylic acids control the wettability of ${\rm TiO}_{2}$ largely through acid-base chemistry at the interface rather than chemisorption on the oxide surface, a finding that can help improve the design of self-cleaning surfaces and photocatalytic devices.

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

Structural insights into β-1,3-glucan cleavage by a glycoside hydrolase family

The fundamental and assorted roles of β-1,3-glucans in nature are underpinned on diverse chemistry and molecular structures, demanding sophisticated and intricate enzymatic systems for their processing. Herein, the selectivity and modes of action of a glycoside hydrolase family active on β-1,3-glucans were systematically investigated combining sequence similarity network, phylogeny, X-ray crystallography, enzyme kinetics, mutagenesis and molecular dynamics. This family exhibits a minimalist and versatile (α/β)-barrel scaffold, which can harbor distinguishing exo or endo modes of action, including an ancillary-binding site for the anchoring of triple-helical β-1,3-glucans. The substrate binding occurs via a hydrophobic knuckle complementary to the canonical curved conformation of β-1,3-glucans or through a substrate conformational change imposed by the active-site topology of some fungal enzymes. Together, these findings expand our understanding of the enzymatic arsenal of bacteria and fungi for the breakdown and modification of β-1,3-glucans, which can be exploited for biotechnological applications.

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

Network structure in alteration layer of boroaluminosilicate glass formed by aqueous corrosion

Exogenously-added LiCl has been shown to slightly accelerate the corrosion rate of a boroaluminosilicate glass called International Simple Glass (ISG) in aqueous solutions over forward- and residual-rate regimes, while KCl and CsCl impede. To understand the effect of exogenously added electrolytes on resulting hydrous species and the network structure of alteration layers, infrared spectroscopy was implemented. It was found that the fraction of molecular water relative to the surface-bound hydroxyl species is lower in the KCl and CsCl conditions compared to the LiCl and pure water conditions. An approximation for the spectral features of the thin surface films from an experimentally-obtained specular-reflectance infrared (SR-IR) spectrum was proposed; results indicate no significant difference in the Si-O bonding network of the alteration layers formed in the presence of exogenously added LiCl, KCl and CsCl. Furthermore, the observed change in corrosion rates might be linked to the relative abundance of molecular water species in the porous network, rather than the silicate bonding structure.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Charge Tethering Drives Intermediate-Range Order and Slow Dynamics in Zwitterionic Liquids

We have synthesized and characterized a family of zwitterionic liquids (ZwLs) based on the poly(ethylene oxide) imidazolium cation and the alkyl sulfonate anion. To better rationalize ZwLs′ structural behavior and networkforming properties, we have computationally studied them in contrast to isostructural but chemically untethered ionic liquid (IL) analogues. Perhaps surprisingly, it is the molecular liquids and not the ILs that show the most selfassembly and intermediate-range order associated with scattering prepeaks; the ZwLs display also the slowest dynamics. This is consistent with prior work showing the importance of networks in considering the viscoelastic relaxation of liquids. We find that, because of their significantly large molecular dipoles, these zwitterions are versatile network-building blocks that result in highly viscous liquids. From a structural perspective, networks in these ZwLs sit somewhere in between what would be true chemical connectivity and the Coulombic networks observed in ionic liquids.

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

Structure and Dynamics of CO 2 at the Air–Water Interface from Classical and Neural Network Potentials

The accurate description of the structure and dynamics of CO 2 at the instantaneous air–water interface, along with the effects of surface fluctuations on the CO 2 -transport processes, is essential for the development of negative emission technologies aimed at minimizing climate change. In this study, we performed molecular dynamics simulations of CO 2 at the air–water interface using neural network potentials (NNPs) trained on ab initio data generated through density-functional-theory-based molecular dynamics simulations. We compared these results with classical force fields to assess their performance in modeling interfacial CO 2 behavior. Our findings revealed that the asymmetric interactions, coupled with thermal surface fluctuations at the air–water interface, significantly influence CO 2 transport into the aqueous phase. The simulations demonstrate that classical force fields underestimate both the free energy of CO 2 transport and the strength of its interactions at the interface compared with the neural network potentials. In conclusion, the free energy and the interfacial dynamics of CO 2 are primarily influenced by the distribution of water within the instantaneous interfacial water layer, responsible for creating an asymmetric intermolecular interaction environment within the interfacial region.

Ab initio molecular dynamics↗
DOE OSTI2021

State predictive information bottleneck

We report the ability to make sense of the massive amounts of high-dimensional data generated from molecular dynamics simulations is heavily dependent on the knowledge of a low-dimensional manifold (parameterized by a reaction coordinate or RC) that typically distinguishes between relevant metastable states, and which captures the relevant slow dynamics of interest. Methods based on machine learning and artificial intelligence have been proposed over the years to deal with learning such low-dimensional manifolds, but they are often criticized for a disconnect from more traditional and physically interpretable approaches. To deal with such concerns, in this work we propose a deep learning based state predictive information bottleneck approach to learn the RC from high-dimensional molecular simulation trajectories. We demonstrate analytically and numerically how the RC learnt in this approach is connected to the committor in chemical physics and can be used to accurately identify transition states. A crucial hyperparameter in this approach is the time delay or how far into the future the algorithm should make predictions about. Through careful comparisons for benchmark systems, we demonstrate that this hyperparameter choice gives useful control over how coarse-grained we want the metastable state classification of the system to be. We thus believe that this work represents a step forward in systematic application of deep learning based ideas to molecular simulations.

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

A Fluorinated Lewis Acidic Organoboron Tunes Polysulfide Complex Structure for High–Performance Lithium–Sulfur Batteries

Many challenges in lithium-sulfur (Li–S) batteries are associated with the radical change in lithium polysulfide (LPS) solubility during cycling, but chemical approaches to address such inconsistency are still lacking. Here, the use of a strong Lewis acidic fluorinated organoboron, tri(2,2,2-trifluoroethyl) borate (TFEB), is reported as a multi-functional mediator to simultaneously overcome multiple technical barriers in practical Li–S batteries. TFEB acts as an anion acceptor and forms strong molecular complexes with Lewis basic LPS. The TFEB-LPS complexes have consistent solubility across the full polysulfide spectrum and deliver several times improved better redox kinetics, unlocking a true redox catalytic mechanism that covers the majority of redox events in thick sulfur cathodes. As a result, Li–S batteries evaluated under practical conditions exhibit significantly improved discharge capacity, rate capability, and cycling stability with the addition of the TFEB additive. More importantly, TFEB also contributes to the stabilization of lithium anode in the presence of polysulfides by generating strong interfacial film. These attributes significantly improve the cycling stability of practical Li–S pouch cells, which are assembled with a unit energy density of 219 Wh kg –1 . Finally, the results provide new molecular insights on the design of unlocking solvation networks of practical Li–S systems.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

A physics-informed operator regression framework for extracting data-driven continuum models

The application of deep learning toward discovery of data-driven models requires careful application of inductive biases to obtain a description of physics which is both accurate and robust. We present here a framework for discovering continuum models from high fidelity molecular simulation data. Our approach applies a neural network parameterization of governing physics in modal space, allowing a characterization of differential operators while providing structure which may be used to impose biases related to symmetry, isotropy, and conservation form. Here, we demonstrate the effectiveness of our framework for a variety of physics, including local and nonlocal diffusion processes and single and multiphase flows. For the flow physics we demonstrate this approach leads to a learned operator that generalizes to system characteristics not included in the training sets, such as variable particle sizes, densities, and concentration.

42 ENGINEERING↗
DOE OSTI2021

Modeling of Peptides with Classical and Novel Machine Learning Force Fields: A Comparison

The replacement of classical force fields (FFs) with novel neural-network-based frameworks is an emergent topic in molecular dynamics (MD) simulations. In contrast to classical FFs, which have proven their capability to provide insights into complex soft matter systems at an atomistic resolution, the machine learning (ML) potentials have yet to demonstrate their applicability for soft materials. However, the underlying philosophy, which is learning the energy of an atom in its surrounding chemical environment, makes this approach a promising tool. In particular for the exploration of novel chemical compounds, which have not been considered in the original parametrization of classical FFs. In this article, we study the performance of the ANI-2x ML model and compare the results with those of two classical FFs, namely, CHARMM27 and the GROMOS96 43a1 FF. We explore the performance of these FFs for bulk water and two model peptides, trialanine and a 9-mer of the α-aminoisobutyric acid, in vacuum and water. The results for water describe a highly ordered water structure, with a structure similar to those using ab initio molecular dynamics simulations. The energy landscape of the peptides described by Ramachandran maps show secondary structure basins similar to those of the classical FFs but differ in the position and relative stability of the basins. Details of the sampled structures show a divergent performance of the different models, which can be related either to the short-ranged nature of the ML potentials or to shortcomings of the underlying data set used for training. These findings highlight the current state of the applicability of ANI-2x ML potential for MD simulations of soft matter systems. Simultaneously, they provide insights for future improvements of current ML potentials.

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

Infrared spectral signatures of interfacial water at TiO 2 –electrolyte interfaces from deep potential molecular dynamics

Vibrational spectroscopy is a powerful tool for probing water at oxide–electrolyte interfaces, but its molecular interpretation can be challenging. Here, we employ deep potential long-range molecular dynamics simulations with layer-resolved spectral analysis to investigate the microscopic origins of the infrared (IR) response of water at the interface with anatase TiO 2 (101), a prototypical oxide surface. The calculated interfacial spectra exhibit characteristic modifications compared to bulk water IR spectra, including enhanced intensities, a red shifted and broadened stretching band, and a higher-frequency shoulder, in qualitative agreement with experiments. Spectral decomposition shows that these signatures originate mainly from the first interfacial water layer, dominated by surface-bound H 2 O at Ti 5C sites, with secondary contributions from the second layer. A moderate salt concentration (0.4 M NaCl) leaves both the interfacial structure and the spectra essentially unchanged, while tuning the pH strongly modulates the spectral intensity. We establish a scaling relation linking the spectral intensity to the surface water dissociation fraction and the dipole moment, both governed by interfacial electric fields. These findings provide a microscopic framework for interpreting IR spectra of oxide–electrolyte interfaces.

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

Structure and dynamics of $\mathrm{Fe_{90}}$$\mathrm{Si_3}$$\mathrm{O_7}$ liquids close to Earth's liquid core conditions

Using an artificial neural-network machine learning interatomic potential, we have performed molecular dynamics simulations to study the structure and dynamics of $\mathrm{Fe_{90}}$$\mathrm{Si_3}$$\mathrm{O_7}$ liquid close to the Earth's liquid core conditions. Here, the simulation results reveal that the short-range structural order (SRO) in the $\mathrm{Fe_{90}}$$\mathrm{Si_3}$$\mathrm{O_7}$ liquid is very strong. About 80% of the atoms are arranged in crystallinelike SRO motifs. In particular, ~ $70$% of Fe-centered clusters can be classified as either hexagonal-close-packed-like or icosahedrallike SRO motifs. The SRO clusters centered on Fe, Si, or O atoms are strongly intermixed and homogenously distributed throughout the liquid. The atomic structure of the liquid and the fractions of dominant SRO clusters are not sensitive to pressure/temperature used in the simulations except that the SRO of the O-centered clusters is enhanced close to inner core pressures. The O-diffusion coefficient is about two to three times larger than the Fe and Si ions and increases more rapidly in the deeper core regions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Learning efficient erasure protocols for an underdamped memory

Here we apply evolutionary reinforcement learning to a simulation model to identify efficient time-dependent erasure protocols for a physical realization of a 1-bit memory using an underdamped mechanical cantilever. We show that these protocols, when applied to the cantilever in the laboratory, are considerably more efficient than our best hand-designed protocols. The learned protocols allow reliable high-speed erasure by minimizing the heating of the memory during its operation. More generally, the combination of methods used here opens the door to the rational design of efficient protocols for various physics applications.

74 ATOMIC AND MOLECULAR PHYSICS↗