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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Magnetic exchange interactions in the van der Waals layered antiferromagnet MnPSe 3

Two-dimensional van der Waals compounds with magnetic ions on a honeycomb lattice are hosts to a variety of exotic behavior. The magnetic interactions in one such compound, MnPSe 3 , are investigated with elastic and inelastic neutron scattering. Magnetic excitations are observed in the magnetically ordered regime and persist to temperatures well above the ordering temperature, T N =74K, consistent with low dimensional magnetic interactions. The inelastic neutron scattering results allow a model spin Hamiltonian to be presented that includes dominant intralayer interactions of J 1ab =0.45 meV, J 2ab =0.03 meV, J 3ab =0.19 meV, consistent with theoretical predictions. Furthermore, despite the quasi-2D behavior, appreciable interlayer interactions of J c =0.031(5) meV are required to model the data. No evidence for anisotropy in the form of a spin gap is observed in the data collected. The measurements on MnPSe 3 are contrasted with those on MnPS 3 and reveal a large increase in the interlayer exchange interaction in MnPSe 3 that may stabilize the similar ordering temperatures in the bulk compounds.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2013

Nano-Ceramic Coated Plastics

Plastic products, due to their durability, safety, and low manufacturing cost, are now rapidly replacing cookware items traditionally made of glass and ceramics. Despite this trend, some still prefer relatively expensive and more fragile ceramic/glassware because plastics can deteriorate over time after exposure to foods, which can generate odors, bad appearance, and/or color change. Nano-ceramic coatings can eliminate these drawbacks while still retaining the advantages of the plastic, since the coating only alters the surface of the plastic. The surface coating adds functionality to the plastics such as self-cleaning and disinfectant capabilities that result from a photocatalytic effect of certain ceramic systems. These ceramic coatings can also provide non-stick surfaces and higher temperature capabilities for the base plastics without resorting to ceramic or glass materials. Titanium dioxide (TiO2) and zinc oxide (ZnO) are the candidates for a nano-ceramic coating to deposit on the plastics or plastic films used in cookware and kitchenware. Both are wide-bandgap semiconductors (3.0 to 3.2 eV for TiO2 and 3.2 to 3.3 eV for ZnO), so they exhibit a photocatalytic property under ultraviolet (UV) light. This will lead to decomposition of organic compounds. Decomposed products can be easily washed off by water, so the use of detergents will be minimal. High-crystalline film with large surface area for the reaction is essential to guarantee good photocatalytic performance of these oxides. Low-temperature processing (<100 C) is also a key to generating these ceramic coatings on the plastics. One possible way of processing nanoceramic coatings at low temperatures (< 90 C) is to take advantage of in-situ precipitated nanoparticles and nanostructures grown from aqueous solution. These nanostructures can be tailored to ceramic film formation and the subsequent microstructure development. In addition, the process provides environment- friendly processing because of the aqueous solution. Low-temperature processing has also shown versatility to generate various nanostructures. The growth of low-dimensional nanostructures (0-D, 1-D) provides a means of enhancing the crystallinity of the solution-prepared films that is of importance for photocatalytic performance. This technology can generate durable, fully functional nano-ceramic coatings (TiO2, ZnO) on plastic materials (silicone, Teflon, PET, etc.) that can possess both photocatalytic oxide properties and flexible plastic properties. Processing cost is low and it does not require any expensive equipment investment. Processing can be scalable to current manufacturing infrastructure.

Cho, Junghyun↗
DOE OSTI2025

Neural Active Manifolds: Nonlinear Dimensionality Reduction for Uncertainty Quantification

We present a new approach for nonlinear dimensionality reduction, specifically designed for computationally expensive mathematical models. We leverage autoencoders to discover a one-dimensional neural active manifold (NeurAM) capturing the model output variability, through the aid of a simultaneously learnt surrogate model with inputs on this manifold. Our method only relies on model evaluations and does not require the knowledge of gradients. The proposed dimensionality reduction framework can then be applied to assist outer loop many-query tasks in scientific computing, like sensitivity analysis and multifidelity uncertainty propagation. In particular, we prove, both theoretically under idealized conditions, and numerically in challenging test cases, how NeurAM can be used to obtain multifidelity sampling estimators with reduced variance by sampling the models on the discovered low-dimensional and shared manifold among models. Several numerical examples illustrate the main features of the proposed dimensionality reduction strategy and highlight its advantages with respect to existing approaches in the literature.

Autoencoders↗
DOE OSTI2023

Accelerating Markov Chain Monte Carlo sampling with diffusion models

Global fits of physics models require efficient methods for exploring high-dimensional and/or multimodal posterior functions. We introduce a novel method for accelerating Markov Chain Monte Carlo (MCMC) sampling by pairing a Metropolis-Hastings algorithm with a diffusion model that can draw global samples with the aim of approximating the posterior. We briefly review diffusion models in the context of image synthesis before providing a streamlined diffusion model tailored towards low-dimensional data arrays. We then present our adapted Metropolis-Hastings algorithm which combines local proposals with global proposals taken from a diffusion model that is regularly trained on the samples produced during the MCMC run. Our approach leads to a significant reduction in the number of likelihood evaluations required to obtain an accurate representation of the Bayesian posterior across several analytic functions, as well as for a physical example based on a global fit of parton distribution functions. Our method is extensible to other MCMC techniques, and we briefly compare our method to similar approaches based on normalising flows. A code implementation can be found at https://github.com/NickHunt-Smith/MCMC-diffusion.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Expanding the interlayer spacing of MXene through nanoarchitecture design

MXenes, a diverse class of two-dimensional transition metal carbides and nitrides with distinct surface chem istries, offer remarkable electrochemical and physicochemical properties. Yet, their performance can be hindered by the intrinsic restacking of layers, which restricts surface activity. This review outlines recent advances in interlayer spacing modulation strategies designed to overcome this limitation. A wide range of guest spe cies–including mono- and polyatomic ions, small molecules, low-dimensional nanomaterials, and polymers–are discussed with respect to their intercalation mechanisms, structural effects, and capacity to introduce functional enhancements. These intercalating species interact with MXene via electrostatic forces, hydrogen bonding, redox reactions, or covalent linkages, resulting in stable, expanded nanoarchitectures. The influence of expanded spacing on capacitive behavior is briefly highlighted to illustrate its practical relevance. Although Ti 3 C 2 T x is currently the most extensively investigated MXene, applying these interlayer engineering strategies to other MXene compositions will be essential for accessing a wider range of properties and functionalities.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Adaptive autoencoder latent space tuning for more robust machine learning beyond the training set for six-dimensional phase space diagnostics of a time-varying ultrafast electron-diffraction compact accelerator

In this work, we present a general adaptive latent space tuning approach for improving the robustness of machine learning tools with respect to time variation and distribution shift. We demonstrate our approach by developing an encoder-decoder convolutional neural network-based virtual 6D phase space diagnostic of charged particle beams in the HiRES ultrafast electron diffraction (UED) compact particle accelerator with uncertainty quantification. Our method utilizes model-independent adaptive feedback to tune a low dimensional 2D latent space representation of ~1 million dimensional objects which are the 15 unique 2D projections (x, y),...,(z, p z ) of the 6D phase space (x, y, z, p x , p y , p z ) of the charged particle beams. We demonstrate our method with numerical studies of short electron bunches utilizing experimentally measured UED input beam distributions.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Evidence of a magnetic transition in atomically thin Cr 2 TiC 2 T x MXene

Two-dimensional (2D) transition metal carbides and nitrides known as MXenes have shown attractive functionalities such as high electronic conductivity, a wide range of optical properties, versatile transition metal and surface chemistry, and solution processability. Although extensively studied computationally, the magnetic properties of this large family of 2D materials await experimental exploration. 2D magnetic materials have recently attracted significant interest as model systems to understand low-dimensional magnetism and for potential spintronic applications. Here, we report on synthesis of Cr 2 TiC 2 T x MXene and a detailed study of its magnetic as well as electronic properties. Using a combination of magnetometry, synchrotron X-ray linear dichroism, and field- and angular-dependent magnetoresistance measurements, we find clear evidence of a magnetic transition in Cr 2 TiC 2 T x at approximately 30 K, which is not present in its bulk layered carbide counterpart (Cr 2 TiAlC 2 MAX phase). Overall, this work presents the first experimental evidence of a magnetic transition in a MXene material and provides an exciting opportunity to explore magnetism in this large family of 2D materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks

Uncertainty quantification (UQ) plays a pivotal role in scientific machine learning, especially when surrogate models are used to approximate complex systems. Although multilayer perceptions (MLPs) are commonly employed as surrogates, they often suffer from overfitting due to their large number of parameters. Kolmogorov-Arnold networks (KANs) offer an alternative solution with fewer parameters. However, gradient-based inference methods, such as Hamiltonian Monte Carlo (HMC), may result in computational inefficiency when applied to KANs, especially for large-scale datasets, due to the high cost of back-propagation. To address these challenges, we propose a novel approach, combining the dropout Tikhonov ensemble Kalman inversion (DTEKI) with Chebyshev KANs. This gradient-free method effectively mitigates overfitting and enhances numerical stability. In addition, we incorporate the active subspace method to reduce the parameter-space dimensionality, allowing us to improve the accuracy of predictions and obtain more reliable uncertainty estimates. Extensive experiments demonstrate the efficacy of our approach in various test cases, including scenarios with large datasets and high noise levels. Our results show that the new method achieves comparable or better accuracy, much higher efficiency as well as stability compared to HMC, in addition to scalability. Moreover, by leveraging the low-dimensional parameter subspace, our method preserves prediction accuracy while substantially reducing further the computational cost.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Quantum magnetism of iron-based ladders: Blocks, spirals, and spin flux

Motivated by increasing experimental evidence of exotic magnetism in low-dimensional iron-based materials, we present a comprehensive theoretical analysis of magnetic states of the multiorbital Hubbard ladder in the orbital-selective Mott phase (OSMP). Here, the model we used is relevant for iron-based compounds of the AFe 2 X 3 family (where A=Cs, Rb, Ba, K are alkali metals and X=S, Se are chalcogenides). To reduce computational effort, and obtain almost exact numerical results in the ladder geometry, we utilize a low-energy description of the Hubbard model in the OSMP—the generalized Kondo-Heisenberg Hamiltonian. Our main result is the doping vs interaction magnetic phase diagram. We reproduce the experimental findings on the AFe 2 X 3 materials, especially the exotic block magnetism of BaFe 2 Se 3 (antiferromagnetically coupled 2×2 ferromagnetic islands of the ↑↑↓↓ form). As in recent studies of the chain geometry, we also unveil block magnetism beyond the 2×2 pattern (with block sizes varying as a function of the electron doping) and also an interaction-induced frustrated block-spiral state (a spiral order of rigidly rotating ferromagnetic islands). Moreover, we predict new phases beyond the one-dimensional system: a robust regime of phase separation close to half filling, incommensurate antiferromagnetism for weak interaction, and a quantum spin-flux phase of staggered plaquette spin currents at intermediate doping. Finally, exploiting the bonding/antibonding band occupations, we provide an intuitive physical picture giving insight into the structure of the phase diagram.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

How machine learning can help select capping layers to suppress perovskite degradation

Environmental stability of perovskite solar cells (PSCs) has been improved by trial-and-error exploration of thin low-dimensional (LD) perovskite deposited on top of the perovskite absorber, called the capping layer. In this study, a machine-learning framework is presented to optimize this layer. We featurize 21 organic halide salts, apply them as capping layers onto methylammonium lead iodide (MAPbI 3 ) films, age them under accelerated conditions, and determine features governing stability using supervised machine learning and Shapley values. We find that organic molecules’ low number of hydrogen-bonding donors and small topological polar surface area correlate with increased MAPbI 3 film stability. The top performing organic halide, phenyltriethylammonium iodide (PTEAI), successfully extends the MAPbI3 stability lifetime by 4 ± 2 times over bare MAPbI 3 and 1.3 ± 0.3 times over state-of-the-art octylammonium bromide (OABr). Through characterization, we find that this capping layer stabilizes the photoactive layer by changing the surface chemistry and suppressing methylammonium loss.

36 MATERIALS SCIENCE↗
DOE OSTI2020

An isolated water droplet in the aqueous solution of a supramolecular tetrahedral cage

Water under nanoconfinement at ambient conditions has exhibited low-dimensional ice formation and liquid–solid phase transitions, but with structural and dynamical signatures that map onto known regions of water’s phase diagram. Using terahertz (THz) absorption spectroscopy and ab initio molecular dynamics, we have investigated the ambient water confined in a supramolecular tetrahedral assembly, and determined that a dynamically distinct network of 9 ± 1 water molecules is present within the nanocavity of the host. The low-frequency absorption spectrum and theoretical analysis of the water in the Ga 4 L 6 12– host demonstrate that the structure and dynamics of the encapsulated droplet is distinct from any known phase of water. A further inference is that the release of the highly unusual encapsulated water droplet creates a strong thermodynamic driver for the high-affinity binding of guests in aqueous solution for the Ga 4 L 6 12– supramolecular construct.

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

Printing noble metal alloy films with compositional gradient

Additive manufacturing has offered great promise in fabricating materials and devices with complex structures and unique properties. In addition to complex structures, additive manufacturing also allows for local changes to material compositions and/or structure, ideal for combinatorial material investigations. Besides, advantages over freedom of design, mass customization, waste minimization and the capability to fast prototyping, the on-demand control of compositional distributions in low dimensional materials remains challenging. In this work, we demonstrate a combinatorial aerosol jet printing technique to create noble metal films with gradient compositions. Nanoparticle sizes and ink chemistry are critical for aerosol jet printing and sintering process in order to achieve defect free thin films. Ruthenium (Ru) and platinum (Pt) nanoparticles with size <5 nm were synthesized and formulated into printable inks. Further, after thermal sintering at temperature as low as 400 °C, printed nanoparticles formed highly reflective films free from microstructural defect. Alloy films of gradient compositions were realized via in-situ mixing Ru and Pt ink streams and varying the mixing ratio during the printing process. This combinatorial printing method provides great potential to rapidly transform nanoparticle inks into new materials compositions and structures unobtainable via conventional methods for high-throughput materials studies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Latent Representation Learning for Structural Characterization of Catalysts

Supervised machine learning-enabled mapping of the X-ray absorption near edge structure (XANES) spectra to local structural descriptors offers new methods for understanding the structure and function of working nanocatalysts. We briefly summarize a status of XANES analysis approaches by supervised machine learning methods. We present an example of an autoencoder-based, unsupervised machine learning approach for latent representation learning of XANES spectra. This new approach produces a lower-dimensional latent representation, which retains a spectrum–structure relationship that can be eventually mapped to physicochemical properties. Furthermore, the latent space of the autoencoder also provides a pathway to interpret the information content “hidden” in the X-ray absorption coefficient. Our approach (that we named latent space analysis of spectra, or LSAS) is demonstrated for the supported Pd nanoparticle catalyst studied during the formation of Pd hydride. By employing the low-dimensional representation of Pd K-edge XANES, the LSAS method was able to isolate the key factors responsible for the observed spectral changes.

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

Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation

Classical molecular dynamics simulates the time evolution of molecular systems through the phase space spanned by the positions and velocities of the constituent atoms. Molecular-level thermodynamic, kinetic, and structural data extracted from the resulting trajectories provide valuable information for the understanding, engineering, and design of biological and molecular materials. The cost of simulating many-body atomic systems makes simulations of large molecules prohibitively expensive, and the high-dimensionality of the resulting trajectories presents a challenge for analysis. Driven by advances in algorithms, hardware, and data availability, there has been a flare of interest in recent years in the applications of machine learning – especially deep learning – to molecular simulation. Furthermore, these techniques have demonstrated great power and flexibility in both extracting mechanistic understanding of the important nonlinear collective variables governing the dynamics of a molecular system, and in furnishing good low-dimensional system representations with which to perform enhanced sampling or develop long-timescale dynamical models. It is the purpose of this article to introduce the key machine learning approaches, describe how they are married with statistical mechanical theory into domain-specific tools, and detail applications of these approaches in understanding and accelerating biomolecular simulation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Optoelectronic properties of bent two-dimensional materials from first-principles methods combined with machine learning

A material’s interaction with light is highly relevant in the design of nanoelectronic devices such as photodiodes, solar cells, photocatalytic cells, phototransistors, and photodetectors. The interaction of a material with light can be altered by mechanical deformation. Fine tuning of the optical properties can be achieved by mechanical bending that alters the electronic structure. Optical properties strongly depend on band gaps, therefore any alteration in the band structure results in a changed optical response of the material. The impact of bending was explored in this project. The goal of this project was to assess the impact of mechanical bending of two-dimensional transition metal dichalcogenides on their optoelectronic properties, using first-principles methods. These first-principles approximations are largely built upon many-body theory for the optical properties of magnetic and topological nanoribbons. GW-BSE is standard for optical absorption, but it is less practical for collective excitations as it was shown in model systems. Time-dependent density functional theory, however, has better promises for collective excitations in low-dimensional materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Exchange bias and interface-related effects in two-dimensional van der Waals magnetic heterostructures: Open questions and perspectives

The exchange bias (EB) effect is known as a fundamentally and technologically important magnetic property of a magnetic bilayer film. It is manifested as a horizontal shift in a magnetic hysteresis loop of a film subject to cooling in the presence of a magnetic field. The EB effect in van der Waals (vdW) heterostructures offers a novel approach for tuning the magnetic properties of the newly discovered single-layer magnets, as well as adds a new impetus to magnetic vdW heterostructures. Indeed, intriguing EB effects have recently been reported in a variety of low-dimensional vdW magnetic systems ranging from a weakly interlayer-coupled vdW magnet (e.g., Fe 3 GeTe 2 ) to a bilayer composed of two different magnetic vdW materials (e.g., Fe 3 GeTe 2 /CrCl 3 , Fe 3 GeTe 2 /FePS 3 , Fe 3 GeTe 2 /MnPS 3 , Fe 3 GeTe 2 /CrSe, Fe 3 GeTe 2 /CrOCl, Fe 3 GeTe 2 /CoPc, Fe 5 GeTe 2 /FePS 3 ), to bilayers of two different vdW defective magnets (e.g., VSe 2 /MoS 2 ), or to metallic ferromagnet/vdW defective magnet interfaces (e.g., Fe/MoS 2 ). Despite their huge potential in spintronic device applications, the physical origins of the observed EB effects have remained elusive to researchers. Here, we present here a critical review of the EB effect and associated phenomena such as magnetic proximity (MP) in various vdW heterostructure systems and propose approaches to addressing some of the emerging fundamental questions.

2D magnets↗
DOE OSTI2022

Controllable Emergent Spatial Spin Modulation in Sr 2 IrO 4 by In Situ Shear Strain

Symmetric anisotropic interaction can be ferromagnetic and antiferromagnetic at the same time but for different crystallographic axes. We show that the competition of anisotropic interactions of orthogonal irreducible representations can be a general route to obtain new exotic magnetic states. We demonstrate it here by observing the emergence of a continuously tunable 12-layer spatial spin modulation when distorting the square-lattice planes in the quasi-two-dimensional antiferromagnetic Sr 2 IrO 4 under in situ shear strain. Furthermore, this translation-symmetry-breaking phase is a result of an unusual strain-activated anisotropic interaction which is at the fourth order and competing with the inherent quadratic anisotropic interaction. Such a mechanism of competing anisotropy is distinct from that among the ferromagnetic, antiferromagnetic, and/or the Dzyaloshinskii-Moriya interactions, and it could be widely applicable and highly controllable in low-dimensional magnets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Autoencoder Neural Network for chemically reacting systems

Incorporating detailed chemical kinetic models is critical for accurate simulations of reacting flows. However, detailed models involve a large number of thermochemical (TC) state variables. Solving the governing equations to evolve these TC variables becomes impractical for real-world applications. In this work, we propose an autoencoder (AE) neural network (NN)-based reduced model to accelerate such simulations. The AE NN is first trained to find a low-dimensional latent representation of the TC states. Then, the evolving state of a chemical system can be tracked by solving the equations of the latent variables instead of the original TC equations. We demonstrate the reduced model in a syngas CO/H 2 combustion system, using training data collected from canonical perfectly stirred reactors (PSRs). It is found that the AE model can reduce the dimension of the combustion system from 12 to 2 while maintaining low reconstruction error and excellent elemental mass conservation for the test dataset. In the a posteriori test, the combustion states obtained from solving the two latent equations are compared to those from solving the 12 equations of the full model. The AE reduced method is found to be able to capture the diverse combustion states on the top two branches of the S-curve well including the extinction turning point, but with higher prediction errors for states near the ignition turning point.

97 MATHEMATICS AND COMPUTING↗