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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Methods to Calculate Electronic Excited-State Dynamics for Molecules on Large Metal Clusters with Many States: Ensuring Fast Overlap Calculations and a Robust Choice of Phase

Here, we present an efficient set of methods for propagating excited-state dynamics involving a large number of configuration interaction singles (CIS) or Tamm-Dancoff approximation (TDA) single-reference excited states. Specifically, (i) following Head-Gordon et al., we implement an exact evaluation of the overlap of singly-excited CIS/TDA electronic states at different nuclear geometries using a biorthogonal basis and (ii) we employ a unified protocol for choosing the correct phase for each adiabat at each geometry. For many-electron systems, the combination of these techniques significantly reduces the computational cost of integrating the electronic Schrodinger equation and imposes minimal overhead on top of the underlying electronic structure calculation. As a demonstration, we calculate the electronic excited-state dynamics for a hydrogen molecule scattering off a silver metal cluster, focusing on high-lying excited states, where many electrons can be excited collectively and crossings are plentiful. Interestingly, we find that the high-lying, plasmon-like collective excitation spectrum changes with nuclear dynamics, highlighting the need to simulate non-adiabatic nuclear dynamics and plasmonic excitations simultaneously. In the future, the combination of methods presented here should help theorists build a mechanistic understanding of plasmon-assisted charge transfer and excitation energy relaxation processes near a nanoparticle or metal surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1995

Convective flow effects on protein crystal growth

During the fourth semi-annual period under this grant we have pursued the following activities: (1) crystal growth morphology and kinetics studies with tetragonal lysozyme. These clearly revealed the influence of higher molecular weight protein impurities on interface shape; (2) characterization of the purity and further purification of lysozyme solutions. These efforts have, for the first time, resulted in lysozyme free of higher molecular weight components; (3) continuation of the salt repartitioning studies with Seikagaku lysozyme, which has a lower protein impurity content that Sigma stock. These efforts confirmed our earlier findings of higher salt contents in smaller crystals. However, less salt is in corporated into the crystals grown from Seikagaku stock. This strongly suggests a dependence of salt repartitioning on the concentration of protein impurities in lysozyme. To test this hypothesis, repartitioning studies with the high purity lysozyme prepared in-house will be begun shortly; (4) numerical modelling of the interaction between bulk transport and interface kinetics. These simulations have produced interface shapes which are in good agreement with out experimental observations; and (5) light scattering studies on under- and supersaturated lysozyme solutions. A consistent interpretation of the static and dynamic data leaves little doubt that pre-nucleation clusters, claimed to exist even in undersaturated solutions, are not present. The article: 'Growth morphology response to nutrient and impurity nonuniformities' is attached.

Rosenberger, Franz↗
DOE OSTI2026

Water-enhanced bifunctional metal-acid catalyst for C=C bond hydrogenation

Water-assisted proton shuttling can promote hydrogenation of polar functional groups, and it is generally believed that such an effect can be hardly applied to hydrogenation of C═C bonds due to the latter's weak interaction with water. Here, we report density functional theory calculations and metadynamics simulations, through which we show a dynamic bifunctional metal–acid site that can be transformed, when interacting with water, into an active configuration for unexpected water-enhanced proton shuttling to C═C bonds. In particular, we investigated B(OH)3 anchored to a Ni catalyst for hydrogenation of cyclohexene in an organic solvent, which showed in experiments an increased rate by 100 times when adding a small amount of water. Metadynamics simulations suggest that a B(OH)3–H2O cluster can form on Ni(111), which promotes the proton transfer in the first hydrogenation step, while the second hydrogenation is still driven by metal-mediated direct H-transfer. The recovery process of B(OH)3–H2O also involves a proton shuttling step. We find that the boric species on the surface serves as an electron reservoir and carries the negative charge to balance the positive charge in the proton transfer steps. This work thus provides fundamental insights into this dynamic transformation process of the metal–acid interface, which can in principle be applied to many other bifunctional systems for hydrogenating non-polar unsaturated groups by engineering the interfacial charge separation.

Sun, Shoutian↗
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 OSTI2026

Quantifying the phase diagram and Hamiltonian of S = 1/2 kagome antiferromagnets: bridging theory and experiment

Spin-1/2 kagome antiferromagnets are leading candidates for realizing quantum spin liquid (QSL) ground states. While QSL ground states are predicted for the pure Heisenberg model, understanding the robustness of the QSL to additional interactions that may be present in real materials is a forefront question in the field. Here we employ large-scale density-matrix renormalization group simulations to investigate the effects of next-nearest neighbor exchange couplings J 2 and Dzyaloshinskii-Moriya interactions D, which are relevant to understanding the prototypical kagome materials herbertsmithite and Zn-barlowite. By utilizing clusters as large as XC12 and extrapolating the results to the thermodynamic limit, we precisely delineate the scope of the QSL phase, which remains robust across an expanded parameter range of J 2 and D. Direct comparison of the simulated static and dynamic spin structure factors with inelastic neutron scattering reveals the parameter space of the Hamiltonians for herbertsmithite and Zn-barlowite, and, importantly, provides compelling evidence that both materials exist within the QSL phase. These results establish a powerful convergence of theory and experiment in this most elusive state of matter.

Jiang, Shengtao [SLAC National Accelerator Laborat↗
NASA NTRS2007

Automated Knowledge Discovery From Simulators

A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.

Burl, Michael↗
NASA NTRS2019

Inflight Parachute Measurement Challenge

The Purpose of the Inflight Parachute Measurement Challenge is to solicit potential measurement solutions that can result in successful parachute loading assumption validation, along with validation of Fluid-Structure Interaction (FSI) simulations. A parachute system contains many components which see various loading conditions including tension and aerodynamic pressure forces. While measurements can be made in wind tunnels, significant limitations exist as compared to flight testing which include scaling, dynamic pressure time history, and cluster behavior. Traditional flight test instrumentation is difficult due the flexible nature of parachute components, packing requirements, and high forces and chaos during deployment. The Objectives of this challenge are to (1) describe the anatomy and behavior of parachute systems, (2) identify measurements of particular interest to the parachute design and analysis community, (3) identify additional uses of measurements such as FSI validation.

Daum, Jared↗
DOE OSTI2023

Response of Sulfonated Polystyrene Melts to Nonlinear Elongation Flows

Ionizable polymers form dynamic networks with domains controlled by two distinct energy scales, ionic interactions and van der Waals forces; both evolve under elongational flows during their processing into viable materials. A molecular level insight of their nonlinear response, paramount to controlling their structure, is attained by fully atomistic molecular dynamics simulations of a model ionizable polymer, polystyrene sulfonate. As a function of increasing elongational flow rate, the systems display an initial elastic response, followed by an ionic fraction-dependent strain hardening, stress overshoot, and eventually strain-thinning. As the sulfonation fraction increases, the chain elongation becomes more heterogeneous. Finally, flow-driven ionic assembly dynamics that continuously break and reform control the response of the system.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Modifying Li + and Anion Diffusivities in Polyacetal Electrolytes: A Pulsed-Field-Gradient NMR Study of Ion Self-Diffusion

Polyacetal electrolytes have been demonstrated as promising alternatives to liquid electrolytes and poly(ethylene oxide) (PEO) for rechargeable lithium-ion batteries; however, the relationship between polymer structure and ion motion is difficult to characterize. Here, we study structure-property trends in ion diffusion with respect to polymer composition for a systematic series of five polyacetals with varying ratios of ethylene oxide (EO) to methylene oxide (MO) units, denoted as P(xEO-yMO), and PEO. We first use 7 Li and 19 F pulsed-field-gradient NMR spectroscopy to measure cation and anion self-diffusion, respectively, in polymer/lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) salt mixtures. At 90 °C, we observe modest changes in Li + diffusivity across all polymer compositions, while anion (TFSI - ) self-diffusion coefficients decrease significantly with increasing MO content. At a given reduced temperature (T - T g ), all polyacetal electrolytes exhibit faster Li + self-diffusion than PEO. Intriguingly, P(EO-MO) and P(EO-2MO) also show slower TFSI - anion self-diffusion than PEO at a given reduced temperature. Molecular dynamics simulations reveal that shorter distances between acetal oxygen atoms (O-CH 2 -O) compared to ether oxygens (O-CH 2 -CH 2 -O) promote more diverse, often asymmetric, Li + coordination environments. Finally, Raman spectra reveal that anion-rich ion clusters in P(EO-MO) and P(EO-2MO) lead to decreased anion diffusivity, which along with increased cation diffusivity, support the viability of polyacetals as high-performance polymer electrolytes.

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

Local-scale Arctic tundra heterogeneity affects regional-scale carbon dynamics

In northern Alaska nearly 65% of the terrestrial surface is composed of polygonal ground, where geomorphic tundra landforms disproportionately influence carbon and nutrient cycling over fine spatial scales. Process-based biogeochemical models used for local to Pan-Arctic projections of ecological responses to climate change typically operate at coarse-scales (1km 2 –0.5°) at which fine-scale (<1km 2 ) tundra heterogeneity is often aggregated to the dominant land cover unit. Here, we evaluate the importance of tundra heterogeneity for representing soil carbon dynamics at fine to coarse spatial scales. We leveraged the legacy of data collected near Utqiagvik, Alaska between 1973 and 2016 for model initiation, parameterization, and validation. Simulation uncertainty increased with a reduced representation of tundra heterogeneity and coarsening of spatial scale. Hierarchical cluster analysis of an ensemble of 21 st -century simulations reveals that a minimum of two tundra landforms (dry and wet) and a maximum of 4km 2 spatial scale is necessary for minimizing uncertainties (<10%) in regional to Pan-Arctic modeling applications.

54 ENVIRONMENTAL SCIENCES↗
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 OSTI2024

Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling

Abstract Machine learning interatomic potentials (MLIPs) enable accurate simulations of materials at scales beyond that accessible by ab initio methods and play an increasingly important role in the study and design of materials. However, MLIPs are only as accurate and robust as the data on which they are trained. Here, we present DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling as an approach to select a robust training set of structures from a large and complex configuration space. By applying DIRECT sampling on the Materials Project relaxation trajectories dataset with over one million structures and 89 elements, we develop an improved materials 3-body graph network (M3GNet) universal potential that extrapolates more reliably to unseen structures. We further show that molecular dynamics (MD) simulations with the M3GNet universal potential can be used instead of expensive ab initio MD to rapidly create a large configuration space for target systems. We combined this scheme with DIRECT sampling to develop a reliable moment tensor potential for titanium hydrides without the need for iterative augmentation of training structures. This work paves the way for robust high-throughput development of MLIPs across any compositional complexity.

Qi, Ji (ORCID:0000000158089931)↗
DOE OSTI2021

Arrested Phase Separation in Double-Exchange Models: Large-Scale Simulation Enabled by Machine Learning

In this work, we present large-scale dynamical simulations of electronic phase separation in the single-band double-exchange model based on deep-learning neural-network potentials trained from small-size exact diagonalization solutions. We uncover an intriguing correlation-induced freezing behavior as doped holes are segregated from half filled insulating background during equilibration. While the aggregation of holes is stabilized by the formation of ferromagnetic clusters through Hund’s coupling between charge carriers and local magnetic moments, this stabilization also creates confining potentials for holes when antiferromagnetic spin-spin correlation is well developed in the background. The dramatically reduced mobility of the self-trapped holes prematurely disrupts further growth of the ferromagnetic clusters, leading to an arrested phase separation. Implications of our findings for phase separation dynamics in materials that exhibit colossal magnetoresistance effect are discussed.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Origin of Unusual Acidity and Li+ Diffusivity in a Series of Water-in-Salt Electrolytes.

Superconcentrated aqueous electrolytes ("water-in-salt" electrolytes, or WiSEs) enable various aqueous battery chemistries beyond the voltage limits imposed by the Pourbaix diagram of water. However, their detailed structural and transport properties remain unexplored and could be better understood through added studies. Here, we report on our observations of strong acidity (pH 2.4) induced by lithium bis(trifluoromethane sulfonyl)imide (LiTFSI) at superconcentration (at 20 mol/kg). Multiple nuclear magnetic resonance (NMR) and pulsed-field gradient (PFG) diffusion NMR experiments, density functional theory (DFT) calculations, and molecular dynamics (MD) simulations reveal that such acidity originates from the formation of nanometric ion-rich structures. The experimental and simulation results indicate the separation of water-rich and ion-rich domains at salt concentrations >= 5 m and the acidity arising therefrom is due to deprotonation of water molecules in the ion-rich domains. As such, the ion-rich domain is composed of hydrophobic -CF3 (of TFSI-) and hydrophilic hydroxyl (OH-) groups. At 20 m concentration, the tortuosity and radius of water diffusion channels are estimated to be similar to 10 and similar to 1 nm, respectively, which are close to values obtained from hydrated Nafion membranes that also have hydrophobic polytetrafluoroethylene (PTFE) backbones and hydrophilic channels consisting of SO3- ion cluster networks providing for the transport of ions and water. Thus, we have discovered the structural similarity between WiSE and hydrated Nafion membranes on the nanometer scale.

Han, Kee Sung↗
DOE OSTI2022

Cluster dynamics modeling of Mn-Ni-Si precipitates coupled with radiation-induced segregation in low-Cu reactor pressure vessel steels

Formation of precipitates enhanced or induced by irradiation causes hardening and embrittlement of nuclear structural materials. Post-irradiation microstructure characterization of reactor pressure vessel (RPV) steels has shown that precipitation can be strongly associated with radiation-induced segregation (RIS) of solutes on dislocations. However, RIS and precipitation have not been coupled in previous precipitation modeling of RPV steels. Here in this study, a new hybrid and spatially-dependent precipitation model is developed that couples cluster dynamics with RIS, providing an unique way to account for concurrent evolution of heterogeneous cluster densities as well as solute and point defect concentration profiles. The model is applied to study the segregation of Mn, Ni, and Si on dislocations and heterogeneous nucleation of Mn-Ni-Si rich precipitates (MNSPs) in a low-Cu RPV steel. The result shows that the onset of MNSP nucleation on dislocations occurs at the fluence of 2 x 10 23 n.m -2 . The number density and mean radius can reach ~ 10 24 m -3 and 1-2 nm at high fluence, respectively. These observations suggest that the role of RIS and heterogeneous nucleation can be significant for RPV steels under a high fluence of irradiation. Results of simulations adopting various dose rates and dislocation densities show an increased fraction of MNSPs on dislocations at the condition of low dose rate, high dislocation density, and high fluence.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Healing of nanocracks by collision cascades in nickel

We use molecular dynamics simulations to investigate the interaction between collision cascades and pre-existing nano-cracks in nickel (Ni). We find that collision cascades cause the cracks to heal whenever a cascade initiates a thermal spike whose core overlaps with the crack. Moreover, crack healing affects the distribution of radiation-induced defects generated during a collision cascade: defect clusters, such as dislocations and stacking fault tetrahedra, dominate in the presence of crack healing, while isolated point defects dominate in its absence. Furthermore, this work indicates that irradiation may reduce the number of pre-existing crack-like flaws in metallic components, potentially improving their mechanical resilience.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The Evolution and Mass Dependence of Galaxy Cluster Pressure Profiles at 0.05 ≤ z ≤ 0.60 and 4 × 10 14 M ⊙ ≤ M 500 ≤ 30 × 10 14 M ⊙

Abstract We have combined X-ray observations from Chandra with Sunyaev–Zel’dovich effect data from Planck and Bolocam to measure intracluster medium pressure profiles from 0.03 R 500 ≤ R ≤ 5 R 500 for a sample of 21 low- z galaxy clusters with a median redshift of 〈 z 〉 = 0.08 and a median mass of 〈 M 500 〉 = 6.1 × 10 14 M ⊙ and a sample of 19 mid- z galaxy clusters with 〈 z 〉 = 0.50 and 〈 M 500 〉 = 10.6 × 10 14 M ⊙ . The mean scaled pressure in the low- z sample is lower at small radii and higher at large radii, a trend that is accurately reproduced in similarly selected samples from The Three Hundred simulations. This difference appears to be primarily due to dynamical state at small radii, evolution at intermediate radii, and a combination of evolution and mass dependence at large radii. Furthermore, the overall flattening of the mean scaled pressure profile in the low- z sample compared to the mid- z sample is consistent with expectations due to differences in the mass accretion rate and the fractional impact of feedback mechanisms. In agreement with previous studies, the fractional scatter about the mean scaled pressure profile reaches a minimum of ≃20% near 0.5 R 500 . This scatter is consistent between the low- z and mid- z samples at all radii, suggesting it is not strongly impacted by sample selection, and this general behavior is reproduced in The Three Hundred simulations. Finally, analytic functions that approximately describe the mass and redshift trends in mean pressure profile shape are provided.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Response of extreme precipitation to uniform surface warming in quasi-global aquaplanet simulations at high resolution

Projections of precipitation extremes in simulations with global climate models are very uncertain in the tropics, in part because of the use of parameterizations of deep convection and model deficiencies in simulating convective organization. Here, we analyse precipitation extremes in high-resolution simulations that are run without a convective parameterization on a quasi-global aquaplanet. The frequency distributions of precipitation rates and precipitation cluster sizes in the tropics of a control simulation are similar to the observed distributions. In response to climate warming, 3 h precipitation extremes increase at rates of up to 9 % K - 1 in the tropics because of a combination of positive thermodynamic and dynamic contributions. The dynamic contribution at different latitudes is connected to the vertical structure of warming using a moist static stability. When the precipitation rates are first averaged to a daily timescale and coarse-grained to a typical global climate-model resolution prior to calculating the precipitation extremes, the response of the precipitation extremes to warming becomes more similar to what was found previously in coarse-resolution aquaplanet studies. However, the simulations studied here do not exhibit the high rates of increase of tropical precipitation extremes found in projections with some global climate models. This article is part of a discussion meeting issue ‘Intensification of short-duration rainfall extremes and implications for flash flood risks’.

Science & Technology - Other Topics↗