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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Quantum simulation of nonequilibrium dynamics and thermalization in the Schwinger model

In this work, we present simulations of nonequilibrium dynamics of quantum field theories on digital quantum computers. As a representative example, we consider the Schwinger model, a (1+1)-dimensional U(1) gauge theory, coupled through a Yukawa-type interaction to a thermal environment described by a scalar field theory. We use the Hamiltonian formulation of the Schwinger model discretized on a spatial lattice. With the thermal scalar fields traced out, the Schwinger model can be treated as an open quantum system and its real-time dynamics are governed by a Lindblad equation in the Markovian limit. The interaction with the environment ultimately drives the system to thermal equilibrium. In the quantum Brownian motion limit, the Lindblad equation is related to a field theoretical Caldeira-Leggett equation. By using the Stinespring dilation theorem with ancillary qubits, we perform studies of both the nonequilibrium dynamics and the preparation of a thermal state in the Schwinger model using IBM's simulator and quantum devices. The real-time dynamics of field theories as open quantum systems and the thermal state preparation studied here are relevant for a variety of applications in nuclear and particle physics, quantum information and cosmology.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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 OSTI2020

Oscillation-like diffusion of two-dimensional liquid dusty plasmas on one-dimensional periodic substrates with varied widths

The long-time diffusion of two-dimensional dusty plasmas on a one-dimensional periodic substrate with varying widths is investigated using Langevin dynamical simulations. When the substrate is narrow and the dust particles form a single row, the diffusion is the smallest in both directions. In this study, we find that as the substrate width gradually increases to twice its initial value, the long-time diffusion of the two-dimensional dusty plasmas first increases, then decreases, and finally increases again, giving an oscillation-like diffusion with varying substrate widths. When the width increases to a specific value, the dust particles within each potential well arrange themselves in a stable zigzag pattern, greatly reducing the diffusion and leading to the observed oscillation in the diffusion with the increase in the width. In addition, the long-time oscillation-like diffusion is consistent with the number of dust particles that are hopping across the potential wells of the substrate.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Molecular level insight into non-bilayer structure formation in thylakoid membranes: a molecular dynamics study

In oxygenic photosynthetic organisms, the light reactions are performed by protein complexes embedded in the lipid bilayer of thylakoid membranes (TMs). The organization of the bulk lipid molecules into bilayer structures provide optimal conditions for the build-up of the proton motive force (pmf) and its utilization for ATP synthesis. However, the lipid composition of TMs is dominated by the non-bilayer lipid species monogalactosyl diacylglycerol (MGDG), and functional plant TMs, besides the bilayer, contain large amounts of non-bilayer lipid phases. Bulk lipids have been shown to be associated with lumenal, stromal-side and marginal-region proteins and proposed to play roles in the self-assembly and photoprotection of the photosynthetic machinery. Furthermore, it has recently been pointed out that the generation and utilization of pmf for ATP synthesis according to the ‘protet’ or protonic charge transfer model Kell (Biochim Biophys Acta Bioenerg 1865(4):149504, 2024), requires high MGDG content Garab (Physiol Plant 177(2):e70230, 2025). In this study, to gain better insight into the structural and functional roles of MGDG, we employed all atom and coarse-grained molecular dynamics simulations to explore how temperature, hydration levels and varying MGDG concentrations affect the structural and dynamic properties of bilayer membranes constituted of plant thylakoid lipids. Our findings reveal that MGDG promotes increased membrane fluidity and dynamic fluctuations in membrane thickness. MGDG-rich stacked bilayers spontaneously formed inverted hexagonal phases; these transitions were enhanced at low hydration levels and at elevated but physiologically relevant temperatures. It can thus be inferred that MGDG plays important roles in heat and drought stress mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Linking Friction Scales from Nano to Macro via Avalanches

Abstract Steady-state fluctuations in the friction force of molybdenum disulfide (MoS 2 ), a prototypical lamellar solid, were analyzed experimentally for newton-scale forces and computationally via molecular dynamics simulations for nanonewton-scale forces. A mean field model links the statics and the dynamics of the friction behavior across these eight orders of magnitude in friction force and six orders of magnitude in friction force fluctuations (i.e., avalanches). Both the statistics and dynamics of the avalanches match model predictions, indicating that friction can be characterized as a series of avalanches with properties that are predictable over a wide range of scales.

Salners, Tyler (ORCID:0000000332647944)↗
DOE OSTI2024

Cavity Controlled Upconversion in CdSe Nanoplatelet Polaritons

Exciton-polaritons provide a versatile platform for investigating quantum electrodynamics effects in chemical systems, such as polariton-altered chemical reactivity. However, using polaritons in chemical contexts will require a better understanding of their photophysical properties under ambient conditions, where chemistry is typically performed. Here, we used cavity quality factor to control strong light–matter interactions and in particular the excited state dynamics of colloidal CdSe nanoplatelets (NPLs) coupled to a Fabry– Pérot optical cavity. With increasing cavity quality factor, we observe significant population of the upper polariton (UP) state, exemplified by the rare observation of substantial UP photoluminescence (PL). Excitation of the lower polariton (LP) states results in upconverted PL emission from the UP branch due to efficient exchange of population between the LP, UP and the reservoir of dark states present in collectively coupled polaritonic systems. In addition, we measure time scales for polariton dynamics ~100 ps, implying great potential for NPL based polariton systems to affect photochemical reaction rates. State-of-the-art quantum dynamical simulations show outstanding quantitative agreement with experiments, and thus provide important insight into polariton photophysical dynamics of collectively coupled nanocrystal-based systems. These findings represent a significant step toward the development of practical polariton photochemistry platforms.

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

The Role of Nonequilibrium Solvent Effects in Enhancing Direct CO 2 Capture at the Air–Aqueous Amino Acid Interface

Direct air capture (DAC) technologies are limited by the poor understanding of the dynamic role of interfaces in modulating the chemisorption of CO 2 from air into solutions. While the reactivity of aqueous amine-based solvents in the bulk environment is strongly inhibited by nonequilibrium solvent effects, promoting DAC at interfaces posits a possibility to reduce the coupling with the solvent and significantly accelerate DAC. Building on an experimentally proven concept to bring an anionic glycine absorbent to the interface through ion-pairing interactions with a positively charged surfactant, we establish the fundamental time scales for key elementary steps involved in DAC with rate theory and enhanced-sampling ab initio molecular dynamics simulations. Here, we elucidate the mechanism by which water influences the free energy barriers and dynamical crossing-recrossing of those barriers, affecting the reaction rates. We find that water reorganizes to partially dehydrate [-NH 2 ], facilitating S N 2-based CO 2 conversion to a zwitterion, which then releases a proton via overhydration of [-NH 2 ]. The low-density interfacial water favors dehydration over overhydration, leading to a comparatively higher barrier (slower kinetics) for proton release. The barrier-recrossing events neutralize this effect, letting both steps occur at the same time scale (sub-microseconds) and making the overall DAC process faster at the interface than in the bulk water. Such an understanding of environment-sensitive solvent effects on the reaction kinetics will help design tailored interfaces for enhanced CO 2 capture kinetics via control of solvation and ion paring.

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

The dynamic adsorption affinity of ligands is a surrogate for the passivation of surface defects

Abstract Surface defects in semiconducting materials, though they have been widely studied, remain a prominent source of loss in optoelectronic devices; here we sought a new angle of approach, looking into the dynamic roles played by surface defects under atmospheric stressors and their chemical passivants in the lifetime of optoelectronic materials. We find that surface defects possess properties distinct from those of bulk defects. ab initio molecular dynamics simulations reveal a previously overlooked reversible degradation mechanism mediated by hydrogen vacancies. We find that dynamic surface adsorption affinity (DAA) relative to surface treatment ligands is a surrogate for passivation efficacy, a more strongly-correlated feature than is the static binding strength emphasized in prior reports. This guides us to design targeted passivator ligands with high molecular polarity: for example, 4-aminobutylphosphonic acid exhibits strong DAA and provides defect passivation applicable to a range of perovskite compositions, including suppressed hydrogen vacancy formation, enhanced photovoltaic performances and operational stability in perovskite solar cells.

Science & Technology - Other Topics↗
DOE OSTI2020

Dynamic Model Reduction for Large-Scale Power Systems Using Wide-Area Measurements

To perform faster than the real-time dynamic simulation of large-scale power systems, it is necessary to reduce the simulated system size by using equivalents for surrounding areas of the study area, and existing dynamic model reduction approach could provide the needed structure of the reduced area. However, further parameter optimization is required to achieve the desired accuracy. In this paper, a particle swarm optimization (PSO) based approach is used to solve the above problem. Parameters for the individual dynamic elements in the reduced system are calibrated repeatedly until the wide-area measurements of the reduced model and the original model are very similar to each other with satisfactory accuracy. Results indicate that after optimization, the dynamic response of the reduced model matches better with that of the original one than using existing methods. Under both the generator-trip event and the bus-fault event, the reduced model has a higher frequency match and less power mismatch.

42 ENGINEERING↗
DOE OSTI2021

A New Distributed Model-Free Control Strategy to Diminish Distribution System Voltage Violations

This paper proposes a new distributed model-free control (MFC) strategy for dynamic voltage control to diminish distribution systems' voltage violations. The objective is to maintain all critical load bus voltages within the acceptable ANSI Range A (+/- 5% of nominal). The distributed MFC strategy, which only requires local voltage measurements from designated load buses, controls online the reactive power generation of available synchronous generator (SG)-based and photovoltaic (PV)-based distributed generators (DGs). The distributed MFC strategy is computationally efficient and does not require modelling of the different system components and disturbances. Time-domain dynamic simulations are conducted for the 21-bus test distribution system fed by multiple DGs to verify the performance of the proposed MFC strategy, and the results are compared against the conventional model-based microgrid voltage stabilizer (MGVS) control strategy. The simulation results show that the distributed MFC strategy provides minimal voltage violations and achieves the dynamic voltage stability of the system under diverse disturbances.

Hatipoglu, Kenan↗
DOE OSTI2021

First principles molecular dynamics maps out complete mineral surface acidity landscape

In “Interfacial structures and acidity constants (pKa) of goethite from first principles molecular dynamics simulations,” authors Y. Zhang, X. Lui, J. Cheng, and X. Lu apply First Principles molecular dynamics (FPMD, also called Density Functional Theory MD, DFT/MD, or ab initio MD, AIMD), to evaluable the complete set of acidity constants (pK a ) of the hydroxyl groups on the most prominent goethite crystal facets. The pK a of these OH and OH$^+_2$ groups are compared with available data from the multisite complexation (MUSIC) model traditionally used to estimate pK a on mineral surfaces. The authors have presented eloquent rationale for the importance and implications of understanding goethite acidity constants in room temperature geochemistry settings. In this paper, I focus on the computational aspects, the strengths of FPMD, and its possibilities.

58 GEOSCIENCES↗
DOE OSTI2024

Ionic Polymers Under Dynamic Conditions

Combining neutron scattering tools and exascale molecular dynamics simulations, research was carried out to resolve the structure and motion of ionic polymers under dynamic conditions. Ionizable polymers constitute a key component in current and potential transformative lightweight energy generation and storage devices. However, the inherent segregation of these polymers to ionic clusters and non-ionic domains, or clusters, that on one hand enables their function, confines the dynamics of the polymer chains, leading to the formation of far-from equilibrium states. These non-equilibrium states morph under in situ conditions, affecting the performance-longevity-safety-cost foundation that underlines the use of these polymers. On route to controlling the assemblies of ionizable polymers into stable functional materials, the research focused on a) understanding the inherent confinement of the polymer chains by the ionic clusters and b) resolving of the response of these polymers to flow fields that are integral to the processing and function of these macromolecules.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Vapor–liquid equilibrium of water with the MB-pol many-body potential

Among the many existing molecular models of water, the MB-pol many-body potential has emerged as a remarkably accurate model, capable of reproducing thermodynamic, structural, and dynamic properties across water's solid, liquid, and vapor phases. In this work, we assessed the performance of MB-pol with respect to an important set of properties related to vapor-liquid coexistence and interfacial behavior. Through direct coexistence classical molecular dynamics simulations at temperatures of 400 K < T < 600 K, we calculated properties such as equilibrium coexistence densities, vapor-liquid interfacial tension, vapor pressure, and enthalpy of vaporization and compared the MB-pol results to experimental data. We also compared rigid vs fully flexible variants of the MB-pol model and evaluated system size effects for the properties studied. We found that the MB-pol model predictions are in good agreement with experimental data, even for temperatures approaching the vapor-liquid critical point; this agreement was largely insensitive to system sizes or the rigid vs flexible treatment of the intramolecular degrees of freedom. Furthermore, these results attest to the chemical accuracy of MB-pol and its high degree of transferability, thus enabling MB-pol's application across a large swath of water's phase diagram.

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

Self-Healing Ceramics and Mechanical Metamaterials

We performed large scale molecular dynamics (MD) simulations and reactive & quantum molecular dynamics simulations to investigate: (1) faceting, grain growth, and crack healing in ceramics; (2) stress-release mechanism and healing in fibers; and (3) mechanical properties of two-dimensional metamaterials. We also designed highly accurate interatomic potentials and forces using artificial neural networks (ANN) and first-principles molecular dynamics simulation data for use in our MD and reactive MD simulations. We demonstrated the efficacy of modeling quantum materials atomistic simulation data using unsupervised deep learning approaches on a quantum computer. These achievements are elaborated in the following sections.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Simulation of polymerization induced phase separation in model thermosets

Polymerization induced phase separation (PIPS) in a three component thermoset is studied using molecular dynamics simulations of a new coarse-grained thermoset model. The system includes two crosslinker molecules, which differ in their glass transition temperatures (T g ) and chain length and thus have the potential for phase separation. Here, one crosslinker has a high T g corresponding to a rubbery behavior, and simulations were performed for a short length (4 beads) and a long length (33 beads). The resin and other crosslinker have low T g . A coarse-grained model is developed with these features and with interaction parameters determined so that for either rubbery crosslinker length, the system is in the liquid state at the cure temperature. For sufficiently slow reaction rates, the long rubbery molecule exhibits PIPS into a bicontinuous array of nanoscale domains, but the short one does not, reproducing recent experimental results. The simulations demonstrate that the reaction rates must be slow enough to allow diffusion to yield phase separation. Particularly, the reaction rate corresponding to the secondary amine must be very slow, else the structure of crosslinked clusters and the substantially increased diffusion time will prevent PIPS.

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

Acclimation of Phytoplankton Fe:C Ratios Dampens the Biogeochemical Response to Varying Atmospheric Deposition of Soluble Iron

Abstract Dissolved iron (dFe) plays an important role in regulating marine productivity. In high nutrient, low chlorophyll regions (>33% of the global ocean), iron is the primary growth limiting nutrient, and elsewhere iron can regulate nitrogen fixation by diazotrophs. The link between iron availability and carbon export is strongly dependent on the phytoplankton iron quotas or cellular Fe:C ratios. This ratio varies by more than an order of magnitude in the open ocean and is positively correlated with ambient dFe concentrations in field observations. Representing Fe:C ratios within models is necessary to investigate how ocean carbon cycling will interact with perturbations to iron cycling in a changing climate. The Community Earth System Model ocean component was modified to simulate dynamic, group‐specific, phytoplankton Fe:C that varies as a function of ambient iron concentration. The simulated Fe:C ratios improve the representation of the spatial trends in the observed Fe:C ratios. The acclimation of phytoplankton Fe:C ratios dampens the biogeochemical response to varying atmospheric deposition of soluble iron, compared to a fixed Fe:C ratio. However, varying atmospheric soluble iron supply has first order impacts on global carbon and nitrogen fluxes and on nutrient limitation spatial patterns. Our results suggest that pyrogenic Fe is a significant dFe source that rivals mineral dust inputs in some regions. Changes in dust flux and iron combustion sources (anthropogenic and wildfires) will modify atmospheric Fe inputs in the future. Accounting for dynamic phytoplankton iron quotas is critical for understanding ocean biogeochemistry and projecting its response to variations in atmospheric deposition.

Environmental Sciences & Ecology↗
DOE OSTI2021

X-composer: enabling cross-environments in-situ workflows between HPC and cloud

As large-scale scientific simulations and big data analyses become more popular, it is increasingly more expensive to store huge amounts of raw simulation results to perform post-analysis. To minimize the expensive data I/O, "in-situ" analysis is a promising approach, where data analysis applications analyze the simulation generated data on the fly without storing it first. However, it is challenging to organize, transform, and transport data at scales between two semantically different ecosystems due to the distinct software and hardware difference. To tackle these challenges, we design and implement the X-Composer framework. X-Composer connects cross-ecosystem applications to form an "in-situ" scientific workflow, and provides a unified approach and recipe for supporting such hybrid in-situ workflows on distributed heterogeneous resources. X-Composer reorganizes simulation data as continuous data streams and feeds them seamlessly into the Cloud-based stream processing services to minimize I/O overheads. For evaluation, we use X-Composer to set up and execute a cross-ecosystem workflow, which consists of a parallel Computational Fluid Dynamics simulation running on HPC, and a distributed Dynamic Mode Decomposition analysis application running on Cloud. Our experimental results show that X-Composer can seamlessly couple HPC and Big Data jobs in their own native environments, achieve good scalability, and provide high-fidelity analytics for ongoing simulations in real-time.

Wang, Dali↗