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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Persistent enhancement of exciton diffusivity in CsPbBr 3 nanocrystal solids

In semiconductors, exciton or charge carrier diffusivity is typically described as an inherent material property. Here, we show that the transport of excitons among CsPbBr 3 perovskite nanocrystals (NCs) depends markedly on how recently those NCs were occupied by a previous exciton. Using transient photoluminescence microscopy, we observe a striking dependence of the apparent exciton diffusivity on excitation laser power that does not arise from nonlinear exciton-exciton interactions or thermal heating. We interpret our observations with a model in which excitons cause NCs to transition to a long-lived metastable configuration that markedly increases exciton transport. The exciton diffusivity observed here (>0.15 square centimeters per second) is considerably higher than that observed in other NC systems, revealing unusually strong excitonic coupling between NCs. The finding of a persistent enhancement in excitonic coupling may help explain other photophysical behaviors observed in CsPbBr 3 NCs, such as superfluorescence, and inform the design of optoelectronic devices.

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

Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker--Planck Equation and Physics-Informed Neural Networks

The Fokker--Planck (FP) equation governing the evolution of the probability density function (PDF) is applicable to many disciplines, but it requires specification of the coefficients for each case, which can be functions of space-time and not just constants and hence require the development of a data-driven modeling approach. When the data available is directly on the PDF, there exist methods for inverse problems that can be employed to infer the coefficients and thus determine the FP equation and subsequently obtain its solution. Herein, we address a more realistic scenario, where only sparse data are given on the particles' positions at a few time instants, which are not sufficient to accurately construct directly the PDF even at those times from existing methods, e.g., kernel estimation algorithms. To this end, we develop a general framework based on physics-informed neural networks (PINNs) that introduces a new loss function using the Kullback--Leibler divergence to connect the stochastic samples with the FP equation to simultaneously learn the equation and infer the multidimensional PDF at all times. In particular, we consider two types of inverse problems, type I, where the FP equation is known but the initial PDF is unknown, and type II, in which, in addition to the unknown initial PDF, the drift and diffusion terms are also unknown. In both cases, we investigate problems with either Brownian or Lévy noise or a combination of both. Here, we demonstrate the new PINN framework in detail in the one-dimensional (1D) case, but we also provide results for up to five dimensions demonstrating that we can infer both the FP equation and dynamics simultaneously at all times with high accuracy using only very few discrete observations of the particles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Transient anisotropic kernel for probabilistic learning on manifolds

PLoM (Probabilistic Learning on Manifolds) is a method introduced in 2016 for handling small training datasets by projecting an Itô equation from a stochastic dissipative Hamiltonian dynamical system, acting as the MCMC generator, for which the KDE-estimated probability measure with the training dataset is the invariant measure. PLoM performs a projection on a reduced-order vector basis related to the training dataset, using the diffusion maps (DMAPS) basis constructed with a time-independent isotropic kernel. In this paper, we propose a new ISDE projection vector basis built from a transient anisotropic kernel, providing an alternative to the DMAPS basis to improve statistical surrogates for stochastic manifolds with heterogeneous data. The construction ensures that for times near the initial time, the DMAPS basis coincides with the transient basis. For larger times, the differences between the two bases are characterized by the angle of their spanned vector subspaces. The optimal instant yielding the optimal transient basis is determined using an estimation of mutual information from Information Theory, which is normalized by the entropy estimation to account for the effects of the number of realizations used in the estimations. Consequently, this new vector basis better represents statistical dependencies in the learned probability measure for any dimension. Three applications with varying levels of statistical complexity and data heterogeneity validate the proposed theory, showing that the transient anisotropic kernel improves the learned probability measure.

Diffusion maps↗
DOE OSTI2021

Multi-fidelity Bayesian neural networks: Algorithms and applications

Here we propose a new class of Bayesian neural networks (BNNs) that can be trained using noisy data of variable fidelity, and we apply them to learn function approximations as well as to solve inverse problems based on partial differential equations (PDEs). These multi-fidelity BNNs consist of three neural networks: The first is a fully connected neural network, which is trained following the maximum a posteriori probability (MAP) method to fit the low-fidelity data; the second is a Bayesian neural network employed to capture the cross-correlation with uncertainty quantification between the low- and high-fidelity data; and the last one is the physics-informed neural network, which encodes the physical laws described by PDEs. For the training of the last two neural networks, we first employ the mean-field variational inference (VI) to maximize the evidence lower bound (ELBO) to obtain informative prior distributions for the hyperparameters in the BNNs, and subsequently we use the Hamiltonian Monte Carlo (HMC) method to estimate accurately the posterior distributions for the corresponding hyperparameters. We demonstrate the accuracy of the present method using synthetic data as well as real measurements. Specifically, we first approximate a one- and four-dimensional function, and then infer the reaction rates in one- and two-dimensional diffusion-reaction systems. Moreover, we infer the sea surface temperature (SST) in the Massachusetts and Cape Cod Bays using satellite images and in-situ measurements. Taken together, our results demonstrate that the present method can capture both linear and nonlinear correlation between the low- and high-fidelity data adaptively, identify unknown parameters in PDEs, and quantify uncertainties in predictions, given a few scattered noisy high-fidelity data. Finally, we demonstrate that we can effectively and efficiently reduce the uncertainties and hence enhance the prediction accuracy with an active learning approach, using as examples a specific one-dimensional function approximation and an inverse PDE problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Spatial coupling of gyrokinetic simulations, a generalized scheme based on first-principles

In this paper, we present a scheme that spatially couples two gyrokinetic codes using first-principles. Coupled equations are presented and a necessary and sufficient condition for ensuring accuracy is derived. This new scheme couples both the field and the particle distribution function. The coupling of the distribution function is only performed once every few time-steps, using a five-dimensional (5D) grid to communicate the distribution function between the two codes. This 5D grid interface enables the coupling of different types of codes and models, such as particle and continuum codes, or delta-f and total-f models. Transferring information from the 5D grid to the marker particle weights is achieved using a new resampling technique. Demonstration of the coupling scheme is shown using two XGC gyrokinetic simulations for both the core and edge. We also apply the coupling scheme to two continuum simulations for a one-dimensional advection–diffusion problem.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Field-level reconstruction from foreground-contaminated 21-cm maps

Current and upcoming 21-cm experiments will soon be able to map 21-cm spatial fluctuations in three dimensions for a wide range of redshifts. However, bright foreground contamination and the nature of radio interferometry create significant challenges, making it difficult to access rich cosmological information from the Fourier modes that lie within the “foreground wedge”. Here, in this work, we introduce two approaches aiming to reconstruct the full 21-cm density field, including the missing modes in the wedge: (a) a field-level inference under an effective field theory (EFT) framework; (b) a diffusion-based deep generative model trained on simulations. Under the EFT framework, we implement a fully differentiable forward model that maps the initial conditions of matter fluctuations to the observed, foreground-filtered 21-cm maps. This enables a gradient-based sampler to simultaneously sample the initial conditions and bias parameters, allowing a physically motivated mode reconstruction. Alternatively, we apply a variational diffusion model to perform 21-cm density reconstruction at the map level. Our model is trained on semi-numerical simulations over a wide range of astrophysical parameters. Our results from both approaches should provide improved cosmological constraints from the field level and also enable cross-correlation between experiments that have little or no overlapping modes.

cosmological perturbation theory↗
DOE OSTI2024

First-principles study of the Stark shift effect on the zero-phonon line of the NV center in diamond

Point defects in semiconductors are attractive candidates for quantum information science applications owing to their ability to act as spin-photon interface or single-photon emitters. However, the coupling between the change of dipole moment upon electronic excitation and stray electric fields in the vicinity of the defect, an effect known as Stark shift, can cause significant spectral diffusion in the emitted photons. In this work, using first principles computations, we revisit the methodology to compute the Stark shift of point defects up to the second order. The approach consists of applying an electric field on a defect in a slab and monitoring the changes in the computed zero-phonon line (i.e., difference in energy between the ground and excited state) obtained from constraining the orbital occupations (constrained-DFT). Here, we study the Stark shift of the negatively charged nitrogen-vacancy (NV) center in diamond using this slab approach. We discuss and compare two approaches to ensure a negatively charged defect in a slab and we show that converged values of the Stark shift measured by the change in dipole moment between the ground and excited states (Δ⁢μ) can be obtained. We obtain a Stark shift of Δ⁢μ = 2.68⁢D using the semilocal GGA-PBE functional and of Δ⁢μ = 2.23⁢D using the HSE hybrid functional. These values are in good agreement with experimental results. We also show that modern theory of polarization can be used on constrained-DFT to obtain Stark shifts in very good agreement with the slab computations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Modeling the Effect of Hydrogen on Tungsten Grain Boundary Properties

The objective of this study is to assess the effect of hydrogen isotopes on mechanical properties of tungsten that are relevant to first-wall applications in a tokamak. We focus on the effects of hydrogen segregation to grain boundaries and its impact on the grain boundary structure and properties. Through molecular dynamics and other atomistic techniques, we are able to make predictions of grain boundary structure and properties related to it, including grain boundary mobility and impurity diffusivity along the grain boundary. The grain boundary structure can change significantly and abruptly as conditions change, including due to heating and radiation damage. These grain boundary phase transformations can have a pronounced effect on mobility and impurity diffusivity, even when no hydrogen is present. How this behavior changes in the presence of hydrogen is not known. The phase may affect the level of hydrogen segregation and hydrogen storage, and the hydrogen may induce significant grain boundary structure changes. Our objective is to predict whether they do. The results inform thermomechanical modeling of tungsten at tokamak conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Assimilating partial observation to enhance feedback control of stochastic dynamical systems

Here, in this paper, we present a novel methodology to tackle feedback optimal control problems in scenarios where the exact state of the controlled process is unknown. It integrates data assimilation techniques and optimal control solvers to manage partial observation of the state process, a common occurrence in practical scenarios. Traditional stochastic optimal control methods assume full state observation, which is often not feasible in real-world fluid dynamics control problems. Our approach underscores the significance of utilizing observational data to inform control policy design. Specifically, we introduce a kernel learning backward stochastic differential equation (SDE) filter to enhance data assimilation efficiency and propose a sample-wise stochastic optimization method within the stochastic maximum principle framework. We demonstrate the efficacy and accuracy of our method in the control of advection-diffusion-reaction flow problem and the Dubins airplane maneuvering problem with model uncertainty.

data driven↗
DOE OSTI2022

Hydrogen trapping energetics at BCC iron-helium interfaces

Density functional theory (DFT) calculations have been performed to assess the trapping and segregation strength of hydrogen (H) to helium (He) interfaces in BCC (Fe) as a function of He density and surface orientation. The He density ranges from 0 to 2 He/V which corresponds to equilibrium bubbles that are expected to form in the structural components in a fusion reactor environment. The DFT calculations consist of a slab of BCC metal and an initial lattice of FCC He. Here, the report on the binding energy of H to these interfaces as well as the migration barriers into and away from the surface, which together provide information on the de-trapping energy for H at He bubbles. It was found that the binding energy of H to He-Fe interfaces decreases with increasing He density. Migration barriers are also modified due to the He density, which are sensitive to the surface orientation as well as the diffusion pathway. These results provide valuable first-principles energetics that are necessary for higher-order, mesoscale models, and provide insight into the extent to which He bubbles may trap tritium in fusion structural materials.

42 ENGINEERING↗
DOE OSTI2021

Using Dissipative Particle Dynamics to Model Effects of Chemical Reactions Occurring within Hydrogels

Computational models that reveal the structural response of polymer gels to changing, dissolved reactive chemical species would provide useful information about dynamically evolving environments. However, it remains challenging to devise one computational approach that can capture all the interconnected chemical events and responsive structural changes involved in this multi-stage, multi-component process. Here, we augment the dissipative particle dynamics (DPD) method to simulate the reaction of a gel with diffusing, dissolved chemicals to form kinetically stable complexes, which in turn cause concentration-dependent deformation of the gel. Using this model, we also examine how the addition of new chemical stimuli and subsequent reactions cause the gel to exhibit additional concentration-dependent structural changes. Through these DPD simulations, we show that the gel forms multiple latent states (not just the “on/off”) that indicate changes in the chemical composition of the fluidic environment. Hence, the gel can actuate a range of motion within the system, not just movements corresponding to the equilibrated swollen or collapsed states. Moreover, the system can be used as a sensor, since the structure of the layer effectively indicates the presence of chemical stimuli.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quantifying local and global mass balance errors in physics-informed neural networks

Physics-informed neural networks (PINN) have recently become attractive for solving partial differential equations (PDEs) that describe physics laws. By including PDE-based loss functions, physics laws such as mass balance are enforced softly in PINN. This paper investigates how mass balance constraints are satisfied when PINN is used to solve the resulting PDEs. We investigate PINN’s ability to solve the 1D saturated groundwater flow equations (diffusion equations) for homogeneous and heterogeneous media and evaluate the local and global mass balance errors. We compare the obtained PINN’s solution and associated mass balance errors against a two-point finite volume numerical method and the corresponding analytical solution. We also evaluate the accuracy of PINN in solving the 1D saturated groundwater flow equation with and without incorporating hydraulic heads as training data. We demonstrate that PINN’s local and global mass balance errors are significant compared to the finite volume approach. Tuning the PINN’s hyperparameters, such as the number of collocation points, training data, hidden layers, nodes, epochs, and learning rate, did not improve the solution accuracy or the mass balance errors compared to the finite volume solution. Mass balance errors could considerably challenge the utility of PINN in applications where ensuring compliance with physical and mathematical properties is crucial.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Structural, Spectroscopic, and Kinetic Insight into the Heating Rate Dependence of Studtite and Metastudtite Dehydration

Uranyl peroxide minerals studtite (UO 2 O 2 ·4H 2 O) and metastudtite (UO 2 O 2 ·2H 2 O) are important materials in the nuclear fuel cycle. When heated, they dehydrate and transform to amorphous uranium oxides (UO x ), yet phase stability and heating rate dependence of these transformations are poorly understood. This information is critical to proper management of fuel cycle materials. In this work, we use in situ powder X-ray diffraction (PXRD), Raman spectroscopy, and thermogravimetric analysis (TGA) to monitor the dehydration of studtite and metastudtite. Strong linear correlation between the heating rate and phase transition temperature is observed. Geometric contraction and diffusion-related kinetic models describe studtite dehydration at slow heating rates, whereas Avrami–Erofeev or reaction order models become more accurate for faster thermal treatments. A second order model describes the transition from metastudtite to UO x regardless of the heating rate. Water retention during studtite dehydration is indicated by PXRD, Raman spectroscopy, and TGA. Here, we observe mixed-phase UO x dehydration products of metastudtite with a likely formation mechanism involving conversion of some uranyl centers from hexagonal to pentagonal bipyramidal coordination units via peroxide liberation. Our observations clarify over 100 years of measurements on these materials and represent an advancement in understanding the chemical behavior of nuclear fuel cycle materials.

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

The persistence of memory in ionic conduction probed by nonlinear optics

Predicting practical rates of transport in condensed phases enables the rational design of materials, devices and processes. This is especially critical to developing low-carbon energy technologies such as rechargeable batteries. For ionic conduction, the collective mechanisms, variation of conductivity with timescales and confinement, and ambiguity in the phononic origin of translation, call for a direct probe of the fundamental steps of ionic diffusion: ion hops. However, such hops are rare-event large-amplitude translations, and are challenging to excite and detect. Here we use single-cycle terahertz pumps to impulsively trigger ionic hopping in battery solid electrolytes. This is visualized by an induced transient birefringence, enabling direct probing of anisotropy in ionic hopping on the picosecond timescale. The relaxation of the transient signal measures the decay of orientational memory, and the production of entropy in diffusion. We extend experimental results using in silico transient birefringence to identify vibrational attempt frequencies for ion hopping. Using nonlinear optical methods, we probe ion transport at its fastest limit, distinguish correlated conduction mechanisms from a true random walk at the atomic scale, and demonstrate the connection between activated transport and the thermodynamics of information.

25 ENERGY STORAGE↗
DOE OSTI2022

Optimization of WAAM Process to Produce AUSC Components with Increased Service Life

Additive manufacturing has the potential to revolutionize industrial hardware and unlock efficiency gains through the fabrication of geometries and architectures not possible by conventional processing. Wire Arc Additive manufacturing (WAAM) process is a class of directed energy deposition process enabling higher build rate and allowing custom wire feedstock allowing spatial variation of microstructure. The larger spot size and lower speed creates a larger melt pool, reducing residual stress and often time creating directional/columnar microstructure for Ni-superalloy. The use of flexible platform provides freedom in deposition strategy, which can accommodate complex substrates, including feature addition onto existing structures, non-flat layers, and repair methods. However, the certification of the final component needs to match the strength requirement. Hence, the quality of the component produced is stringently monitored to avoid buildup of residual stress, cracks, porosity and to reduce detrimental segregated phases commonly observed during alloy solidification. Simulation plays a huge role in predicting the melt pool dimension and can be used to optimize the process parameter. Similar development is also required to perform physics-based modeling of microstructural development in WAAM that can predict the microstructural features during solidification and can be used to optimize the process more effectively. To move toward this goal, Raytheon Technologies Research Center together with Siemens worked to create a set of computational tools to control the process parameters, enable on-line measurements and acquisition with feedback to the optimized process parameters, and eventually track material evolution through each step of the additive process. Computational fluid dynamics is used for accurate prediction and calibration of the thermal field during WAAM process. and phase field models for microstructure evolution as a function of processing parameters to establish a connection between additive parameters and the final microstructure. Here we report cellular automata (CA) model development to predict the dendritic microstructure evolution with surface and bulk nuclei for single track and multiple layers. The CA model was developed to account for secondary element addition and predict segregation, local melting, and latent heat release as well as prediction of Euler angles from orientation information and validated against experiments. This framework was utilized to tailor spatially-varying composition in a part by appropriately controlling the microstructure evolution during the additive process. Functionally graded Haynes 282 alloy with high Cr content at the surface was tested for oxidation and mechanical properties. An advanced physics-based reaction-diffusion model predicting the simultaneous creation of chromium oxide and alumina is developed and validated to extend life expectancy of the WAAM manufactured high temperature part. A machine-learning data-driven framework establishing the process-structure relationship from a dataset of real microstructure images and corresponding process history data has been developed and implemented in the NX Siemens design system. The digital twin configuration along with the tool path generation enabled prediction of WAAM component buildup time and the techno-economic analysis provided a favorable option for all 4 cases with 15-40% cost reduction.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Atomistically-informed modeling of point defect clustering and evolution in irradiated ThO 2

A cluster dynamics (CD) model has been developed to investigate the nucleation and growth of point defect clusters, i.e., interstitial prismatic loops and nanoscale and sub-nanoscale voids, in ThO 2 during irradiation by energetic particles. The model considers cluster off-stoichiometry due to the asymmetry of point defect generation on the O and Th sublattices under irradiation, as well as the point defect diffusivities and the defect binding energies to clusters. The energies were established using detailed molecular dynamics simulations considering the statistical variability of cluster configuration. A high-order adaptive time-integration has been used to solve the model. The predicted loop density and their average size is in good agreement with reported experimental observations for proton irradiated ThO 2 at 600°C. In conclusion, the model did not predict void evolution due to the sluggish kinetics of cation vacancies, explaining the absence of voids in proton irradiated ThO 2 (and other oxides) at relatively low temperatures.

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

Stochastic fluctuations in relativistic fluids: Causality, stability, and the information current

We develop a general formalism for introducing stochastic fluctuations around thermodynamic equilibrium which takes into account, for the first time, recent developments in the causality and stability properties of relativistic hydrodynamic theories. The method is valid for any covariantly stable theory of relativistic viscous fluid dynamics derived from a covariant maximum entropy principle. We illustrate the formalism with some applications, showing how it could be used to consistently introduce fluctuations in a model of relativistic heat diffusion and in conformally invariant Israel-Stewart theory in a general hydrodynamic frame. Furthermore, the latter example is used to study the hydrodynamic frame dependence of the symmetric two-point function of fluctuations of the energy-momentum tensor.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Electrochemical Reactivity of Hydrogen Storage Materials: Exploring Borohydride and Hydrazinium Salt Mixtures

Electrochemical characterization of hydrogen storage materials was conducted in a non-aqueous environment to investigate the direct electrochemical release and consumption of hydrogen and the potential for regeneration. We first address the challenge of minimal solubility of the synthetic precursors, sodium borohydride (NaBH 4 ) and hydrazinium bromide (N 2 H 5 Br), in both organic and inorganic solvents. We next determine and calibrate a reference electrode formulation compatible with our non-aqueous media and analytes that demonstrates a stable reference potential. We employ cyclic voltammetry (CV) to characterize the precursors and mixtures thereof. Each CV peak is assigned to a corresponding electrochemical reaction. Using the rate-dependent CV method and Randles–Ševčík equation, we calculate the diffusion coefficient of each chemical (NaBH 4 and N 2 H 5 Br). Analysis of the CVs, coupled with 11B NMR analysis, reveals a room temperature chemical transformation of NaBH 4 and N 2 H 5 Br mixtures into hydrazine borane (N 2 H 4 BH 3 ). These results are particularly significant, considering the limited information available on the electrochemical characterization of metal borohydride and hydrazinium salt in non-aqueous media. This work establishes a foundation for adapting a non-aqueous electrochemical system to further study the borohydride family of chemistries and to design and develop electrochemical devices for direct electrical and chemical energy interconversion with hydrogen storage materials.

08 HYDROGEN↗