Search NASA⌕ Search

SEARCH · Search NASA

Results for “network”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Tensor Network Path Integral Study of Dynamics in B850 LH2 Ring with Atomistically Derived Vibrations

The recently introduced multisite tensor network path integral (MS-TNPI) allows simulation of extended quantum systems coupled to dissipative media. We use MS-TNPI to simulate the exciton transport and the absorption spectrum of a B850 bacteriochlorophyll (BChl) ring. The MS-TNPI network is extended to account for the ring topology of the B850 system. Accurate molecular-dynamics-based description of the molecular vibrations and the protein scaffold is incorporated through the framework of Feynman–Vernon influence functional. To relate the present work with the excitonic picture, an exploration of the absorption spectrum is done by simulating it using approximate and topologically consistent transition dipole moment vectors. Comparison of these numerically exact MS-TNPI absorption spectra are shown with second-order cumulant approximations. Finally, the effect of temperature on both the exact and the approximate spectra is also explored.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Neutron Spin–Echo Studies of the Structural Relaxation of Network Liquid ZnCl 2 at the Structure Factor Primary Peak and Prepeak

Using neutron spin-echo spectroscopy, we studied the microscopic structural relaxation of a prototypical network ionic liquid ZnCl 2 at the structure factor primary peak and pre-peak. The results show that the relaxation at the primary peak is faster than the pre-peak and the activation energy is ≈33% higher. Stretched exponential relaxation is observed even at temperatures well above the melting point T m . Surprisingly, the stretching exponent shows a rapid increase upon cooling, especially at the primary peak, where it changes from stretched exponential to simple exponential on approaching T m . Furthermore, these results suggest that the appearance of glassy dynamics typical of the supercooled state even in the equilibrium liquid state of ZnCl 2 as well as the difference of activation energy at the two investigated length scales are related to the formation of network structure on cooling.

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

Designing Glass and Crystalline Phases of Metal–Bis(acetamide) Networks to Promote High Optical Contrast

Owing to their high tunability and predictable structures, metal–organic materials offer a powerful platform to study glass formation and crystallization processes and to design glasses with unique properties. In this work, we report a novel series of glass-forming metal–ethylenebis(acetamide) networks that undergo reversible glass and crystallization transitions below 200 °C. The glass-transition temperatures, crystallization kinetics, and glass stability of these materials are readily tunable, either by synthetic modification or by liquid-phase blending, to form binary glasses. Pair distribution function (PDF) analysis reveals extended structural correlations in both single and binary metal–bis(acetamide) glasses and highlights the important role of metal–metal correlations during structural evolution across glass–crystal transitions. Notably, the glass and crystalline phases of a Co–ethylenebis(acetamide) binary network feature a large reflectivity contrast ratio of 4.8 that results from changes in the local coordination environment around Co centers. These results provide new insights into glass–crystal transitions in metal–organic materials and have exciting implications for optical switching, rewritable data storage, and functional glass ceramics.

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

Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture

Abstract Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li 7 P 3 S 11 , a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.

Chemistry↗
DOE OSTI2018

Hyperexpandable, self-healing macromolecular crystals with integrated polymer networks

Formation of condensed matter typically involves a trade-off between structural order and flexibility. As the extent and directionality of interactions between atomic or molecular components increase, materials generally become more ordered but less compliant and vice versa. Yet, high levels of structural order and flexibility need not necessarily be mutually exclusive: there are many biological (e.g., microtubules, flagella, viruses) and synthetic assemblies (e.g., dynamic molecular crystals and frameworks) that can undergo considerable structural transformations without losing their crystalline order and possess remarkable mechanical properties with utility in diverse applications such as selective sorption, separation, sensing, and mechanoactuation. Ultimately, however, the extent of structural changes by such flexible crystals and their elasticity are constrained by the necessity to maintain a continuous network of bonding interactions between the constituents of the lattice. Consequently, even the most dynamic porous materials tend to be brittle and isolated as microcrystalline powders,14 and flexible organic/inorganic molecular crystals cannot expand without fracturing. Owing to their rigidity, crystalline materials rarely display self-healing behavior. Here we report that macromolecular ferritin crystals with integrated hydrogel polymers can isotropically expand to 180% of their original dimensions and >500% of their original volume while retaining periodic order and faceted Wulff morphologies. Even after the separation of neighboring ferritin molecules by 50 Å upon lattice expansion, specific molecular contacts between them can be reformed upon lattice contraction, resulting in the recovery of atomic-level periodicity and the highest resolution ferritin structure reported. Dynamic bonding interactions between the hydrogel network and the ferritin molecules endow the crystals with the ability to resist fragmentation and self-heal efficiently, while the chemical tailorability of the ferritin molecules enables the creation of chemically and mechanically differentiated domains within single crystals.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Silicon-on-glass pore network micromodels with oxygen-sensing fluorophore films for chemical imaging and defined spatial structure

Pore network microfluidic models were fabricated by a silicon-on-glass technique that provides the precision advantage of dry etched silicon while creating a structure that is transparent across all microfluidic channels and pores, and can be imaged from either side. A silicon layer is bonded to an underlying borosilicate glass substrate and thinned to the desired height of the microfluidic channels and pores. The silicon is then patterned and through-etched by deep reactive ion etching (DRIE), with the underlying glass serving as an etch stop. After bonding on a transparent glass cover plate, one obtains a micromodel in oxygen impermeable materials with water wet surfaces where the microfluidic channels are transparent and structural elements such as the pillars creating the pore network are opaque. The micromodel can be imaged from either side. The advantageous features of this approach in a chemical imaging application are demonstrated by incorporating a Pt porphyrin fluorophore in a PDMS film serving as the oxygen sensing layer and a bonding surface, or in a polystyrene film coated with a PDMS layer for bonding. The sensing of a dissolved oxygen gradient was demonstrated using fluorescence lifetime imaging, and it is shown that different matrix polymers lead to optimal use in different ranges dissolved oxygen concentration. Imaging with the opaque pillars in between the observation direction and the continuous fluorophore film yields images that retain spatial information in the sensor image.

chemical imaging↗
DOE OSTI2015

Facile one-pot synthesis of porphyrin based porous polymer networks (PPNs) as biomimetic catalysts

Stable porphyrin based porous polymer networks, PPN-23 and PPN-24, have been synthesized through a facile one-pot approach by the aromatic substitution reactions of pyrrole and aldehydes. We report PPN-24(Fe) shows high catalytic efficiency as a biomimetic catalyst in the oxidation reaction of 2,2'-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) in the presence of H 2 O 2 .

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

Large-scale experimental validation of thermochemical water-splitting oxides discovered by defect graph neural networks

Thermochemical water-splitting (TCH) based on 2-step thermal redox cycles in metal oxides is a promising approach to generating H 2 , but state-of-the-art (SOTA) CeO 2 has several practical limitations, which has motivated continued materials discovery efforts in this field. Here, in this study, we improve upon a SOTA defect graph neural network (dGNN) surrogate model's oxygen vacancy predictions and combine them with materials project phase diagrams to down-select and discover structurally diverse, experimentally known metal oxides whose TCH performance was previously unknown. Amongst twelve candidates selected based on our high-throughput screening and down-selection criteria, we achieved ∼80% accuracy in identifying materials with stable redox cycling and hydrogen production in stagnation flow reactor water-splitting experiments. Closer to 100% accuracy can be achieved if higher-accuracy, hybrid DFT-predicted vacancy formation energies were computed and used in lieu of the most uncertain dGNN-based screening predictions, as they correct false positives to true negatives. Notably, two discovered candidates, Sr 3 PrMn 2 O 8 and Ba 2 Fe 2 O 5 , display hydrogen yields greater than CeO 2 under specific redox conditions. In conclusion, these results demonstrate our ability to computationally predict and experimentally validate promising candidate TCH materials that have the potential to compete with CeO 2 .

08 HYDROGEN↗
DOE OSTI2016

Water-induced nanochannel networks in self-assembled block ionomers

Block ionomers cast from solution exhibit solvent-templated morphologies that can be altered by solvent-vapor annealing. When cast from a mixed solvent, a midblock-sulfonated pentablock ionomer self-assembles into spherical ionic microdomains that are loosely connected. Upon exposure to liquid water, nanoscale channels irreversibly develop between the microdomains due to swelling and form a continuous mesoscale network. In this work, we use electron tomography and real-time X-ray scattering to follow this transformation and show that the resultant morphology provides a highly effective diffusive pathway.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Mapping the Temperature-dependent and network site-specific onset of spectral diffusion at the surface of a water cluster cage

We explore the kinetic processes that sustain equilibrium in a microscopic, finite system. This is accomplished by monitoring the spontaneous, time-dependent frequency evolution (the frequency autocorrelation) of a single OH oscillator, embedded in a water cluster held in a temperature-controlled ion trap. The measurements are carried out by applying two-color, IR-IR photodissociation mass spectrometry to the D3O+?(HDO)(D2O)19 isotopologue of the “magic number” protonated water cluster, H+?(H2O)21. The OH group can occupy any one of the five spectroscopically distinct sites in the distorted pentagonal dodecahedron cage structure. The OH frequency is observed to evolve over tens of milliseconds in the temperature range (90-120 K). Starting at 100 K, large “jumps” are observed between two OH frequencies separated by ~300 cm-1 indicating migration of the OH group from the bound OH site at 3350 cm-1 to the free position at 3686 cm-1. Increasing the temperature to 110 K leads to partial interconversion among many sites. All sites are observed to interconvert at 120 K such that the distribution of the unique OH group among them adopts the form one would expect for a canonical ensemble. The spectral dynamics displayed by the clusters thus offer an unprecedented view into the molecular-level processes that drive spectral diffusion in an extended network of water molecules.

Yang, Nan↗
DOE OSTI2018

Evaluation of regression and neural network models for solar forecasting over different short-term horizons

Forecasting solar irradiation has acquired immense importance in view of the exponential increase in the number of solar pho- tovoltaic (PV) system installations. In this article, analyses results involving statistical and machine-learning techniques to predict solar irradiation for di?erent forecasting horizons are reported. Yearlong typical meteorological year 3 (TMY3) datasets from three cities in the United States with di?erent climatic conditions have been used in this analysis. A simple forecast approach that assumes consecutive days to be identical serves as a baseline model to compare forecasting alternatives. To account for seasonal variability and to capture short-term ?uctuations, di?erent variants of the lagged moving average (LMX) model with cloud cover as the input variable are evaluated. Finally, the proposed LMX model is evaluated against an arti?cial neural network (ANN) model. How the one-hour and 24-hour models can be used in conjunction to predict di?erent short-term rolling horizons is discussed, and this joint application is illustrated for a four-hour rolling horizon forecast scheme. Finally, the e?ect of using predicted cloud cover values, instead of measured ones, on the accuracy of the models is assessed. Results show that LMX models do not degrade in forecast accuracy if models are trained with the forecast cloud cover data.

Inanlouganji, Alireza↗