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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Spin-orbit coupling induced demagnetization in Ni: Ab initio nonadiabatic molecular dynamics perspective

Spin-orbit coupling (SOC), which can induce spin flip during the relaxation of photoexcited charge carrier, plays a crucial role in spin dynamics. Here in this work, we have used time-domain ab initio nonadiabatic molecular dynamics (NAMD) method to study the SOC induced ultrafast demagnetization in Ni at 300K. The spin-diabatic representation using spin-polarized Kohn-Sham (KS) basis sets and spin-adiabatic representation using spinor basis sets have been applied, and both of them achieve demagnetization in Ni with a timescale around 100fs. The spin-diabatic representation suggests a picture that the electron-phonon coupling (EPC) provides direct energy relaxation channel among the same-spin states, while the SOC can induce spin flip. After photoexcitation, it is found the spin-minority electrons relax to the same-spin states rather than the opposite-spin states, since EPC is larger than SOC by one order of magnitude. By contrast, for the spin-majority electrons, spin flip occurs since there are no empty same-spin states as electron acceptor above the Fermi level. The different relaxation pathways for spin-majority and spin-minority electrons induce the demagnetization. The spin-adiabatic representation provides an Elliott-Yafet spin-phonon scattering picture. The SOC induced reduction of magnetic moment in Ni may induce magnon to drive further demagnetization. The ab initio NAMD simulation provides a critical angle to understand how the SOC and EPC affect demagnetization process in Ni.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Chatter Stability of Machining Operations

Here, this paper reviews the dynamics of machining and chatter stability research since the first stability laws were introduced by Tlusty and Tobias in the 1950s. The paper aims to introduce the fundamentals of dynamic machining and chatter stability, as well as the state of the art and research challenges, to readers who are new to the area. First, the unified dynamic models of mode coupling and regenerative chatter are introduced. The chatter stability laws in both the frequency and time domains are presented. The dynamic models of intermittent cutting, such as milling, are presented and their stability solutions are derived by considering the time-periodic behavior. The complexities contributed by highly intermittent cutting, which leads to additional stability pockets, and the contribution of the tool's flank face to process damping are explained. The stability of parallel machining operations is explained. The design of variable pitch and serrated cutting tools to suppress chatter is presented. The paper concludes with current challenges in chatter stability of machining which remains to be the main obstacle in increasing the productivity and quality of manufactured parts.

42 ENGINEERING↗
DOE OSTI2023

Tuning charge carrier dynamics through spacer cation functionalization in layered halide perovskites: an ab initio quantum dynamics study

Dion–Jacobson (DJ) phase two-dimensional (2D) hybrid halide perovskites are promising for environmentally stable optoelectronic device applications due to their attractive photophysical properties, including long charge carrier lifetime and partially suppressed non-radiative losses. However, the atomistic details of the structure–property relationship are severely limited, which substantially restricts the strategic materials designed for these layered halide perovskites (LHPs). Here, we combine nonadiabatic molecular dynamics and time-domain density functional theory to understand the effects of spacer cation functionalization on the ground and excited-state charge carrier dynamics in DJ phase LHPs. Our in-depth study reveals that the fluorination of spacer cations considerably restricts the thermal motions of LHPs at ambient conditions. The compact structure weakens the overall electron–phonon interactions and reduces the thermal fluctuations of the band edges in real-time. These dynamic modifications partially mitigate the non-radiative recombination, prolonging the lifetime of photogenerated charge carriers. The suppressed carrier loss mechanism strongly suggests that the fluorinated spacer cation-based LHPs would exhibit enhanced performance as optoelectronic materials. These systematic simulations of excited state carrier dynamics elaborate that chemically viable functionalization of the spacer cations is a robust approach to improve the photophysical properties of 2D halide perovskites strategically. These insights also guide us to propose a few potential strategies to design highly beneficial spacer cations of LHPs that can be introduced in next-generation optoelectronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Temperature-dependent anisotropic magnetoresistance and spin-torque-driven vortex dynamics in a single microdisk

Spin-orbit-torque-driven dynamics have recently gained interest in the field of magnetism due to the reduced requirement of current densities and an increase in efficiency, as well as the ease of implementation of different devices and materials. From a practical point of view, the low-frequency dynamics below 1GHz is particularly interesting since dynamics associated with magnetic domains lie in this frequency range. While spin-torque excitation of high-frequency modes has been extensively studied, the intermediate low-frequency dynamics have received less attention, although spin torques could potentially be used for both manipulation of the spin texture and the excitation of dynamics. In this paper, we demonstrate that it is possible to drive magnetic vortex dynamics in a single microdisk by spin-Hall torque at varying temperatures and relate the results to transport properties. We find that the gyrotropic mode of the core couples to the low-frequency microwave signal and produces a measurable voltage. The dynamic measurements are in agreement with magnetic transport measurements and are supported by micromagnetic simulations. Our results open the door for integrating magnetic vortex devices in spintronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Real Space Imaging of Spin Stripe Domain Fluctuations in a Complex Oxide

Understanding the formation and dynamics of charge and spin-ordered states in low-dimensional transition metal oxide materials is crucial to understanding unconventional high-temperature superconductivity. La 2-x Sr x NiO 4+δ (LSNO) has attracted much attention due to its interesting spin dynamics. Recent x-ray photon correlation spectroscopy studies have revealed slow dynamics of the spin order (SO) stripes in LSNO. Here, we applied resonant soft x-ray ptychography to map the spatial distribution of the SO stripe domain inhomogeneity in real space. The reconstructed images show the SO domains are spatially anisotropic, in agreement with previous diffraction studies. For the SO stripe domains, it is found that the correlation lengths along different directions are strongly coupled in space. Surprisingly, fluctuations were observed in the real space amplitude signal, rather than the phase or position. Overall, we attribute the observed slow dynamics of the stripe domains in LSNO to thermal fluctuations of the SO domain boundaries.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Explaining and predicting human behavior and social dynamics in simulated virtual worlds: reproducibility, generalizability, and robustness of causal discovery methods

Ground Truth program was designed to evaluate social science modeling approaches using simulation test beds with ground truth intentionally and systematically embedded to understand and model complex Human Domain systems and their dynamics Lazer et al. (Science 369:1060–1062, 2020). Our multidisciplinary team of data scientists, statisticians, experts in Artificial Intelligence (AI) and visual analytics had a unique role on the program to investigate accuracy, reproducibility, generalizability, and robustness of the state-of-the-art (SOTA) causal structure learning approaches applied to fully observed and sampled simulated data across virtual worlds. In addition, we analyzed the feasibility of using machine learning models to predict future social behavior with and without causal knowledge explicitly embedded. In this paper, we first present our causal modeling approach to discover the causal structure of four virtual worlds produced by the simulation teams—Urban Life, Financial Governance, Disaster and Geopolitical Conflict. Our approach adapts the state-of-the-art causal discovery (including ensemble models), machine learning, data analytics, and visualization techniques to allow a human-machine team to reverse-engineer the true causal relations from sampled and fully observed data. We next present our reproducibility analysis of two research methods team’s performance using a range of causal discovery models applied to both sampled and fully observed data, and analyze their effectiveness and limitations. We further investigate the generalizability and robustness to sampling of the SOTA causal discovery approaches on additional simulated datasets with known ground truth. Our results reveal the limitations of existing causal modeling approaches when applied to large-scale, noisy, high-dimensional data with unobserved variables and unknown relationships between them. We show that the SOTA causal models explored in our experiments are not designed to take advantage from vasts amounts of data and have difficulty recovering ground truth when latent confounders are present; they do not generalize well across simulation scenarios and are not robust to sampling; they are vulnerable to data and modeling assumptions, and therefore, the results are hard to reproduce. Finally, when we outline lessons learned and provide recommendations to improve models for causal discovery and prediction of human social behavior from observational data, we highlight the importance of learning data to knowledge representations or transformations to improve causal discovery and describe the benefit of causal feature selection for predictive and prescriptive modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Pb dimerization greatly accelerates charge losses in MAPbI 3 : Time-domain ab initio analysis

Metal halide perovskites constitute a new type of semiconducting materials with long charge carrier lifetimes and efficient light-harvesting. The performance of perovskite solar cells and related devices is limited by nonradiative charge and energy losses, facilitated by defects. Combining nonadiabatic molecular dynamics and time-domain density functional theory, we demonstrate that charge losses depend strongly on the defect chemical state. By considering an extra Pb atom in CH 3 NH 3 PbI 3 , which is a common defect in lead halide perovskites, we investigate its influence on charge trapping and recombination. In a chemically inert form as a Pb interstitial, the extra Pb atom has only a mild influence on charge recombination. However, if the extra Pb atom binds to a native Pb atom to form a dimer, the charge trapping and recombination are greatly accelerated because the Pb-dimer creates a localized midgap trap state that couples strongly to the perovskite valence band edge. Holes disappear from the valence band two orders of magnitude faster than in the pristine perovskite and recombine with conduction band electrons one order of magnitude faster. The simulations identify the phonon modes involved in the nonradiative charge trapping and recombination and highlight the importance of rapid decoherence within the electronic subsystem for long carrier lifetimes. The detailed atomistic analysis of the charge trapping and recombination mechanisms enriches the understanding of defect properties and provides theoretical guidance for improving perovskite performance.

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

A Graph Dynamical neural network approach for decoding dynamical states in ferroelectrics.

Ferroelectric materials such as BaTiO 3 show tremendous potential for emerging advances in memory devices, particular neuromorphic type devices. High density of memory can be obtained by stabilising polar domain walls at the nanoscale, regions of discontinuity between the well-defined polarization order parameter, but little is known about what controls their structure and dynamics in real nanoscale materials. Indeed, chiral polar domain walls have been observed in heterogeneous ferroelectrics, such as oxygen-deficient BaTiO 3 , but very little is known about how such polar-domains walls interact with defects. Indeed, a critical understanding of how dynamics of domain-walls depend on point-defects is crucial to create engineered ferroelectric memory devices. For this work, we perform large-scale simulations of nansocale domain-wall dynamics in pristine and defective BaTiO 3 using reactive force-field developed by us earlier (Phys. Chem. Chem. Phys., 2019, 21, 18240–18249), and capture their dynamical dependence on point defects using a graph dynamical neural-network approach, which we adapted to interrogate solids with well-defined order-parameters, and implemented using Pytorch based libraries. Our machine learning (ML) approach goes beyond the traditional post-processing methods to capture both spatial and temporal heterogeneities of large-scale molecular dynamics simulations of complex defective ferroelectric oxide materials. We crucially find that isolated oxygen vacancies introduce very localized spatial regions (~1–2 unit-cell in length) that show slow dipole relaxation due to formation of defect-dipoles, and that these defect-dipoles in turn slow the intrinsic dynamics of domain walls. Further, the roughness of domain walls, also influenced by vacancies, introduce dynamic heterogeneity along the domain-wall. As such we find a novel mechanism by which quenched disorder due to defects introduce dynamic heterogeneity thereby influencing response to external fields (particularly time varying fields) in a ferroelectric. Our study also emphasizes the need for creating digital twins of dynamical quantities to achieve autonomous in operando control of nanoscale switching.

42 ENGINEERING↗
DOE OSTI2025

Axion interactions with domain and bubble walls

We show that interactions between axion-like particles (ALPs) and co-dimension one defects, such as phase-transition bubble walls and solitonic domain walls, can lead to important changes in the evolution of both walls and ALPs. The leading effect arises from the change in the ALP decay constant across the interface, which naturally follows from shift-symmetric interactions with the corresponding order parameter. Specifically, we show that for thin walls moving relativistically, an ALP background — such as e.g. axion dark matter — gives rise to a frictional force on the interface that is proportional to γ 2 , with γ the Lorentz factor of the wall, and that this effect is present in both the oscillating and frozen axion regimes. We explore the broader consequences of this effect for bubble and domain walls in the early universe, and show that this source of friction can be present even in the absent of a conventional medium such as radiation or matter. Possible implications include modifications to the dynamics of bubble and domain walls and their corresponding gravitational wave signatures, as well as the generation of a dark radiation component of ALPs in the form of ultra-relativistic ‘axion shells’ with Lorentz factor γ shell ≃ 2γ 2 ≫ 1 that may remain relativistic until the present day.

Axions and ALPs↗
DOE OSTI2021

Condensation of collective polar vortex modes

The dynamics of extended objects such as domain walls, domain bubbles, vortex structures, etc., can be described by their equations of motion associated with their effective mass and spring constant. Here we analytically derive the equations of motion for the polarization dynamics and elastodynamics for the structural responses of ferroelectric polar vortices, and theoretically extract their effective mass, spring constant, and mode frequencies. We demonstrate two subterahertz phonon modes and predicted their frequencies, both consistent with our recent experimental measurements and phase-field simulations. We show that elastic modulation of the energy function and spring constants leads to a condensation of a collective mode upon a second-order structural transition from symmetric to asymmetric vortices at a critical strain, analogous to the ferroelectric soft phonon mode at a ferroelectric transition. Furthermore, the present work offers a theoretical framework for predicting and manipulating the ultrafast collective dynamics of polar nanostructures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

kynema-fmb [SWR-23-07]

Kynema-FMB (FKA: Kynema) is an open-source performance portable flexible multibody (FMB) dynamics solver designed for time-domain simulations. While originally tailored for wind turbine structural dynamics, the formulation and implementation are those of a general flexible-multidbody dynamics solver that can readily be applied to a wide range of systems. Kynema was designed with a narrow focus, namely to provide a lightweight, fast, accurate FMD solver for coupling to computational-fluid-dynamics (CFD) codes, especially the CFD codes in the Kynema suite, for fluid-structure-interaction (FSI) simulations. Kynema-FMB is equipped to model systems that can be represented as a collection of beams and rigid bodies that are connected through constraints. Degrees of freedom are defined in the inertial/global frame of reference and include displacements and rotations (formally as rotation matrices, but stored as quaternions). The underlying formulation is built on a Lie-group time integrator designed for index-3 differential-algebraic equations, which is second-order accurate in time (Bruls et al., 2012). Beam models are based on geometrically exact beam theory and are discretized as high-order spectral finite elements similar to those in BeamDyn (Wang et al., 2017). The governing equations for a FMD system like a wind turbine constitute a highly nonlinear system of constrained partial-differential equations. Kynema-FMB uses analytical Jacobians in the nonlinear-system solves in each time step. Linear systems use sparse storage and several third-party sparse-linear-system solvers are enabled. Ill conditioning of linear systems is mitigated with preconditioning described in Bottasso et al, 2008. Kynema-FMB is integrated with a simple open-source controller (ROSCO). There is an application programming interface (API) for coupling to geometry-resolved CFD (like that in Sharma et al., 2023) and actuator-force CFD (like that in Kuhn et al., 2025). In the latter, for actuator-line models, Kynema-FMB includes an internal blade-element solver that depends on user-provided lookup tables for coefficients of lift and drag, i.e., aerodynamic polars. Kynema-FMB is written in C++ and leverages Kokkos and Kokkos-Kernels (KokkosEcosystem) as its performance portability layer enabling simulations on both CPU and GPU systems. The repository is equipped with extensive automated testing at the unit and regression/system levels. The following describes the high-level development objectives conceived for Kynema: *Kynema will follow modern software development best practices, including test-driven development (TDD), version control, hierarchical automated testing, and continuous integration (CI) for a robust development environment. *The core data structures are memory efficient and enable vectorization and parallelization at multiple levels. *Data structures are data-oriented to exploit methods for accelerated computing including high utilization of chip resources (e.g., single instruction multiple data (SIMD) instruction sets) and parallelization using GP-GPUs. *The computational algorithms incorporate robust open-source libraries for mathematical operations, resource allocation, and data management. *The API design considers multiple stakeholder needs and ensure integration with existing and future ecosystems for data science, machine learning, and AI. *Kynema-FMB is written in modern C++ and leverages Kokkos as its performance-portability library with inspiration from the kynema stack.

Sprague, MichaelA.↗
DOE OSTI2020

Antiferromagnetic domain switching modulated by an ultrathin Co interlayer in the Fe/Co/CoO/MgO(001) system

Using a combination of hysteresis loop, Kerr microscope, and x-ray magnetic circular dichroism measurements, in this study we investigated the antiferromagnetic (AFM) domain switching process modulated by the sub-nm thick Co inserting layer in a single crystalline Fe/Co/CoO/MgO(001). The CoO AFM domain switching occurs at lower temperature for the thicker Co interlayer, and the activation energy barrier of CoO AFM domain switching decreases as the Co interlayer thickness increases. The exchange coupling strength between the AFM spins in CoO layer and the ferromagnetic spins in the Fe/Co bilayer is found to be independent of the Co layer thickness. Our results suggest an approach to modulate the dynamic properties of AFM domains with an interfacial modification.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Self-energy dynamics and the mode-specific phonon threshold effect in Kekulé-ordered graphene

Abstract Electron-phonon interaction and related self-energy are fundamental to both the equilibrium properties and non-equilibrium relaxation dynamics of solids. Although electron-phonon interaction has been suggested by various time-resolved measurements to be important for the relaxation dynamics of graphene, the lack of energy- and momentum-resolved self-energy dynamics prohibits direct identification of the role of specific phonon modes in the relaxation dynamics. Here, by performing time- and angle-resolved photoemission spectroscopy measurements on Kekulé-ordered graphene with folded Dirac cones at the Γ point, we have succeeded in resolving the self-energy effect induced by the coupling of electrons to two phonons at Ω1 = 177 meV and Ω2 = 54 meV, and revealing its dynamical change in the time domain. Moreover, these strongly coupled phonons define energy thresholds, which separate the hierarchical relaxation dynamics from ultrafast, fast to slow, thereby providing direct experimental evidence for the dominant role of mode-specific phonons in the relaxation dynamics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Deception-Based Cyber Attacks on Hierarchical Control Systems using Domain-Aware Koopman Learning

Industrial control systems are subject to cyber attacks that produce physical consequences. These attacks can be both hard to detect and protracted. Here, we focus on deception-based sensor bias attacks made against a hierarchical control system where the attacker attempts to be stealthy. We develop a a data-driven, optimization-based attacker model and use the Koopman operator to represent the system dynamics in a domain-aware and computationally efficient manner. Using this model, we compute several different attacks against a high-fidelity commercial building emulator and compare the impacts of those attacks to each other. Finally, we discuss some computational considerations and identify avenues for future research.

koopman operator, Cyber-Physical Security, machine↗
DOE OSTI2021

Learning to Correct Climate Projection Biases

The fidelity of climate projections is often undermined by biases in climate models due to their simplification or misrepresentation of unresolved climate processes. While various bias correction methods have been developed to post-process model outputs to match observations, existing approaches usually focus on limited, low-order statistics, or break either the spatiotemporal consistency of the target variable, or its dependency upon model resolved dynamics. We develop a Regularized Adversarial Domain Adaptation (RADA) methodology to overcome these deficiencies, and enhance efficient identification and correction of climate model biases. Instead of pre-assuming the spatiotemporal characteristics of model biases, we apply discriminative neural networks to distinguish historical climate simulation samples and observation samples. The evidences based on which the discriminative neural networks make distinctions are applied to train the domain adaptation neural networks to bias correct climate simulations. We regularize the domain adaptation neural networks using cycle-consistent statistical and dynamical constraints. An application to daily precipitation projection over the contiguous United States shows that our methodology can correct all the considered moments of daily precipitation at approximately $1^\circ$ resolution, ensures spatiotemporal consistency and inter-field correlations, and can discriminate between different dynamical conditions. Our methodology offers a powerful tool for disentangling model parameterization biases from their interactions with the chaotic evolution of climate dynamics, opening a novel avenue toward big-data enhanced climate predictions.

58 GEOSCIENCES↗
DOE OSTI2022

Cryo-EM structure of transmembrane AAA+ protease FtsH in the ADP state

Abstract AAA+ proteases regulate numerous physiological and cellular processes through tightly regulated proteolytic cleavage of protein substrates driven by ATP hydrolysis. FtsH is the only known family of membrane-anchored AAA+ proteases essential for membrane protein quality control. Although a spiral staircase rotation mechanism for substrate translocation across the FtsH pore has been proposed, the detailed conformational changes among various states have not been clear due to absence of FtsH structures in these states. We report here the cryo-EM structure for Thermotoga maritima FtsH ( Tm FtsH) in a fully ADP-bound symmetric state. Comparisons of the ADP-state structure with its apo-state and a substrate-engaged yeast YME1 structure show conformational changes in the ATPase domains, rather than the protease domains. A reconstruction of the full-length Tm FtsH provides structural insights for the dynamic transmembrane and the periplasmic domains. Our structural analyses expand the understanding of conformational switches between different nucleotide states in ATP hydrolysis by FtsH.

59 BASIC BIOLOGICAL SCIENCES↗