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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Magnetic domain engineering in SrRuO3 thin films

Abstract Magnetic domain engineering in ferromagnetic thin films is a very important route toward the rational design of spintronics and memory devices. Although the magnetic domain formation has been extensively studied, artificial control of magnetic domain remains challenging. Here, we present the control of magnetic domain formation in paradigmatic SrRuO 3 /SrTiO 3 heterostructures via structural domain engineering. The formation of structural twin domains in SrRuO 3 films can be well controlled by breaking the SrTiO 3 substrate symmetry through engineering miscut direction. The combination of x-ray diffraction analysis of structural twin domains and magnetic imaging of reversal process demonstrates a one-to-one correspondence between structural domains and magnetic domains, which results in multi-step magnetization switching and anomalous Hall effect in films with twin domains. Our work sheds light on the control of the magnetic domain formation via structural domain engineering, which will pave a path toward desired properties and devices applications.

Materials Science↗
DOE OSTI2023

Physics-guided neural networks with engineering domain knowledge for hybrid process modeling

As neural networks are more frequently used to solve problems in science and engineering, the methods used to incorporate scientific knowledge into these networks are becoming increasingly complex. Here, this work breaks down these complicated techniques into a set of basic strategies which can easily be applied to diverse situations. Several novel neural networks are built using the categories laid out in this work. These networks are tested on simulated data from a continuous stirred tank reactor (CSTR) model to evaluate the advantages provided by each network. The three points demonstrated in this work are: (1) architectural hybrid models can speed up convergence and reduce the amount of data necessary to train a model; (2) adding a physics-guided loss function can improve model generalization and make models more physically consistent; (3) using physics-guided initialization and transfer learning improves accuracy and speeds up convergence, but can harm generalizability if used incorrectly.

42 ENGINEERING↗
DOE OSTI2026

Unconventional magnon-mediated spin torque enabled by ferroelectric domain engineering in multiferroic BiFeO 3

Spin current provides an energy-efficient approach for manipulating magnetization, when its spin polarization aligns with the magnetization direction. However, conventional spin-source materials possess high crystalline symmetry, restricting spin polarization to be orthogonal to both spin and charge current directions. Here, we overcome this limitation by utilizing the concept of magnon-mediated spin-orbit torque through integration of the insulating multiferroic BiFeO 3 with a conventional spin-source material. We observe that spin polarization generated by conventional spin-source material can excite unconventional magnon polarization due to the interplay between cycloidal antiferromagnetic order and the ferroelectric domain structure in BiFeO 3 . This produces an unconventional magnon torque that allows deterministic, field‑free switching of in‑plane magnetization collinear with the current direction, unattainable with conventional spin-source materials. Our results establish multiferroic-based heterostructure as a symmetry‑engineered magnon spin source, paving the way for low-power spintronic devices.

Liang, Yuhan [Tsinghua Univ., Beijing (China); Cor↗
DOE OSTI2022

Morphological-informed Thermal Property Prediction in the Engineering Domain

Using first-principle, atomic scale methods to predict thermal carrier (electron and phonon) behavior at nanoscale yields highly resolved thermal properties. Density functional theory simulations can model myriad effects on the transport of phonons and electrons (e.g., phonon and electron coupling, electron correlation, defect presence, carrier scattering), and yield thermal properties for small geometric domains such as thermal and electrical conductivity, or heat capacity. While these thermal properties are accurate in atomistic and nanoscopic systems, they must be scaled up to the microstructural domain to be a useful predictor for experimental basis, as the microscale is where myriad changes and physical phenomenon occur within a material (e.g., grain boundaries, precipitate aggregations, interfaces, defect clusters). My talk will discuss the necessity of thermal property predictions for nuclear fuels applications and more. Additionally I will discuss the necessity in developing multi-physics, multi-scale methods for future applications in predictions for not only nuclear fuels, but other materials applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Advancing the Performance of Lithium-Rich Oxides in Cooperation with Inherent Complexities: Engineering Domain Structure

The nanodomain structure of lithium- and manganese-rich, composite cathode materials has been systematically altered through synthesis conditions while keeping larger-scale morphological differences to a minimum. Clear changes in electrochemical performance across the samples studied are observed, especially with respect to the anomalous impedance at low states-of-charge. Atomic-scale modeling, coupled to electrochemical measurements and physical characterization, reveals the local consequences of the high-voltage activation charge process as a function of specific domain structures. Furthermore, the results explain the influence of different postactivation structures on the insertion of Li-ions and the associated impedance during discharge. This work adds to our series of studies on lithium- and manganese-rich oxides, demonstrating that the inherent performance of LMRs can be greatly enhanced through rational, highly controlled synthetic strategies based on an understanding of synthesis–structure–property relationships across length scales.

25 ENERGY STORAGE↗
DOE OSTI2021

Antiphase-Boundary-Engineered Domain Switching in a (110)-Oriented BiFeO 3 Film

A ferroelectric domain wall memory device exploits the presence (or absence) of domain walls as the basis for information storage. Although structural defects strongly influence ferroelectric domain wall nucleation and growth, this correlation is far from being fully understood. For example, a single defect can play opposing roles: it can act as a nucleation site to initiate domain switching or, conversely, serve as a pinning center inhibiting domain wall motion. This is particularly the case for antiphase boundaries (APBs), whose influence on switching remains relatively unexplored despite being ubiquitous in oxide perovskite ferroelectrics. Here, we use aberration-corrected scanning transmission microscopy and in situ transmission electron microscopy under an applied bias to extensively characterize the influence of an APB on the switching behavior in a (110)-oriented BiFeO 3 thin film. We demonstrate that deterministic nucleation occurs in the proximity of a vertically oriented APB, following which domain growth occurs laterally in a symmetric fashion. Atomic-scale structural analysis reveals that the shear operation vector for the APB is 1/2[110]; this half-unit-cell shift extends over a few nanometers, implying that the APB plane is inclined toward the imaging direction. The symmetric switching observation is attributed to local inhomogeneous lattice strain and a reduction of polarization caused by the APB. These results demonstrate that an extended structural defect can be used as an active switching element rather than a stumbling block in reprogrammable domain-wall-based devices. Ultimately, this will expand the capabilities of ferroelectric thin films for future electronic devices based on ferroelectric domain walls.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Ferroelectric Domain Wall Engineering Enables Thermal Modulation in PMN–PT Single Crystals

Acting like thermal resistances, ferroelectric domain walls can be manipulated to realize dynamic modulation of thermal conductivity ( k ), which is essential for developing novel phononic circuits. Despite the interest, little attention has been paid to achieving room-temperature thermal modulation in bulk materials due to challenges in obtaining a high thermal conductivity switching ratio ( k high / k low ), particularly in commercially viable materials. Here, room-temperature thermal modulation in 2.5 mm-thick Pb(Mg 1/3 Nb 2/3 )O 3 – x PbTiO 3 (PMN– x PT) single crystals is demonstrated. With the use of advanced poling conditions, assisted by the systematic study on composition and orientation dependence of PMN– x PT, a range of thermal conductivity switching ratios with a maximum of ≈1.27 is observed. Further, simultaneous measurements of piezoelectric coefficient (d 33 ) to characterize the poling state, domain wall density using polarized light microscopy (PLM), and birefringence change using quantitative PLM reveal that compared to the unpoled state, the domain wall density at intermediate poling states (0< d 33 33,max ) is lower due to the enlargement in domain size. At optimized poling conditions (d 33,max ), the domain sizes show increased inhomogeneity that leads to enhancement in the domain wall density. This work highlights the potential of commercially available PMN– x PT single crystals among other relaxor-ferroelectrics for achieving temperature control in solid-state devices.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Bayesian, Multifidelity Operator Learning for Complex Engineering Systems–A Position Paper

Abstract Deep learning has significantly improved the state-of-the-art in computer vision and natural language processing, and holds great potential to design effective tools for predicting and simulating complex engineering systems. In particular, scientific machine learning seeks to apply the power of deep learning to scientific and engineering tasks, with operator learning (OL) emerging as a particularly effective tool. OL can approximate nonlinear operators arising in complex engineering systems, making it useful for simulating, designing, and controlling those systems. In this position paper, we provide a comprehensive overview of OL, including its potential applications to complex engineering domains. We cover three variations of OL approaches: deterministic OL for modeling nonautonomous systems, OL with uncertainty quantification (UQ) capabilities, and multifidelity OL. For each variation, we discuss drawbacks and potential applications to engineering, in addition to providing a detailed explanation. We also highlight how multifidelity OL approaches with UQ capabilities can be used to design, optimize, and control engineering systems. Finally, we outline some potential challenges for OL within the engineering domain.

Computer Science↗
DOE OSTI2025

Polarization rotation in a ferroelectric BaTiO 3 film through low-energy He-implantation

Domain engineering in ferroelectric thin films is crucial for next-generation microelectronic and photonic technologies. Here, a method is demonstrated to precisely control domain configurations in BaTiO 3 thin films through low-energy He ion implantation. The approach transforms a mixed ferroelectric domain state with significant in-plane polarization into a uniform out-of-plane tetragonal phase by selectively modifying the strain state in the film’s top region. This structural transition significantly improves domain homogeneity and reduces polarization imprint, leading to symmetric ferroelectric switching characteristics. The demonstrated ability to manipulate ferroelectric domains post-growth enables tailored functional properties without compromising the coherently strained bottom interface. The method’s compatibility with semiconductor processing and ability to selectively modify specific regions make it particularly promising for practical implementation in integrated devices. This work establishes a versatile approach for strain-mediated domain engineering that could be extended to a wide range of ferroelectric systems, providing new opportunities for memory, sensing, and photonic applications where precise control of polarization states is essential.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Polarization rotation in a ferroelectric BaTiO$_3$ film through low-energy He-implantation

Domain engineering in ferroelectric thin films is crucial for next-generation microelectronic and photonic technologies. Here, a method is demonstrated to precisely control domain configurations in BaTiO$_3$ thin films through low-energy He ion implantation. The approach transforms a mixed ferroelectric domain state with significant in-plane polarization into a uniform out-of-plane tetragonal phase by selectively modifying the strain state in the film's top region. This structural transition significantly improves domain homogeneity and reduces polarization imprint, leading to symmetric ferroelectric switching characteristics. The demonstrated ability to manipulate ferroelectric domains post-growth enables tailored functional properties without compromising the coherently strained bottom interface. The method's compatibility with semiconductor processing and ability to selectively modify specific regions make it particularly promising for practical implementation in integrated devices. This work establishes a versatile approach for strain-mediated domain engineering that could be extended to a wide range of ferroelectric systems, providing new opportunities for memory, sensing, and photonic applications where precise control of polarization states is essential.

Luo, Liang [Ames Laboratory (AMES), Ames, IA (Unit↗
DOE OSTI2020

Validation of (not-historical) large-event near-fault ground-motion simulations for use in civil engineering applications

Ground-motion simulations generated from physics-based wave propagation models are gaining increasing interest in the engineering community for their potential to inform the performance-based design and assessment of infrastructure residing in active seismic areas. A key prerequisite before the ground-motion simulations can be used with confidence for application in engineering domains is their comprehensive and rigorous investigation and validation. This article provides a four-step methodology and acceptance criteria to assess the reliability of simulated ground motions of not historical events, which includes (1) the selection of a population of real records consistent with the simulated scenarios, (2) the comparison of the distribution of Intensity Measures (IMs) from the simulated records, real records, and Ground-Motion Prediction Equations (GMPEs), (3) the comparison of the distribution of simple proxies for building response, and (4) the comparison of the distribution of Engineering Demand Parameters (EDPs) for a realistic model of a structure. Specific focus is laid on near-field ground motions (<10km) from large earthquakes (M w 7), for which the database of real records for potential use in engineering applications is severely limited. The methodology is demonstrated through comparison of (2490) near-field synthetic records with 5 Hz resolution generated from the Pitarka et al (2019) kinematic rupture model with a population of (38) pulse-like near-field real records from multiple events and, when applicable, with NGA-W2 GMPEs. Lastly, the proposed procedure provides an effective method for informing and advancing the science needed to generate realistic ground-motion simulations, and for building confidence in their use in engineering domains.

42 ENGINEERING↗
DOE OSTI2024

A Database of Stress-Strain Properties Auto-generated from the Scientific Literature using ChemDataExtractor

Abstract There has been an ongoing need for information-rich databases in the mechanical-engineering domain to aid in data-driven materials science. To address the lack of suitable property databases, this study employs the latest version of the chemistry-aware natural-language-processing (NLP) toolkit, ChemDataExtractor, to automatically curate a comprehensive materials database of key stress-strain properties. The database contains information about materials and their cognate properties: ultimate tensile strength, yield strength, fracture strength, Young’s modulus, and ductility values. 720,308 data records were extracted from the scientific literature and organized into machine-readable databases formats. The extracted data have an overall precision, recall and F-score of 82.03%, 92.13% and 86.79%, respectively. The resulting database has been made publicly available, aiming to facilitate data-driven research and accelerate advancements within the mechanical-engineering domain.

Kumar, Pankaj↗
DOE OSTI2025

DS-TIDE: Harnessing Dynamical Systems for Efficient Time-Independent Differential Equation Solving

Time-Independent Differential Equations (TIDEs) are central to modeling equilibrium behavior across a wide range of scientific and engineering domains, from electrostatics to porous media flow. Conventional numerical solvers offer reliable solutions but incur significant computational costs due to fine-grained discretization and iterative procedures. Machine learning-based approaches address this by replacing iterative solving processes with one-time inference; however, their sophisticated models require extensive training resources that often exceed those of traditional solvers. Consequently, designing a TIDE solver that achieves high accuracy, broad applicability, and exceptional computational efficiency remains a fundamental challenge. In this paper, we propose DS-TIDE, a novel hardware solver that is inspired by, and subsequently leverages, the intrinsic connection between Dynamical Systems (DS) and Differential Equations (DEs) to efficiently and accurately solve TIDEs. DS-TIDE employs a CMOS-compatible DS-based processor, whose physical states evolve under carefully designed DE-driven dynamics and naturally converge to equilibrium -- the solution of the target TIDE -- within ~1µs on a ~1-watt DS-TIDE processor. To enhance expressivity, DS-TIDE incorporates Heterogeneous Dynamics with Temporal Layering (HDTL), which solves TIDEs through a three-stage DS evolution -- conditioning, solving, and decoding -- each governed by specialized dynamics. The entire evolution process is analogous to an infinitely deep neural network temporally unrolled, offering the system the capability of representing complex equations. Furthermore, DS-TIDE is equipped with an on-device DS-DE Auto-Alignment mechanism that dynamically adapts intrinsic hardware dynamics within milliseconds, effectively aligning the system’s dynamics to diverse target DEs. Experimental results across TIDEs from a wide range of scientific and engineering domains demonstrate that DS-TIDE achieves ~10^3× speedup, ~10^5× energy savings, and competitive or superior accuracy compared to state-of-the-art numerical and ML-based solvers.

Liu, Chuan↗