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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Moment-based adaptive time integration for thermal radiation transport

Here, in this paper we develop a framework for moment-based adaptive time integration of deterministic multifrequency thermal radiation transpot (TRT). We generalize our recent semi-implicit-explicit (IMEX) integration framework for gray TRT to multifrequency TRT, and also introduce a semi-implicit variation that facilitates higher-order integration of TRT, where each stage is implicit in all components except opacities. To appeal to the broad literature on adaptivity with Runge–Kutta methods, we derive new embedded methods for four asymptotic preserving IMEX Runge–Kutta schemes we have found to be robust in our previous work on TRT and radiation hydrodynamics. We then use a moment-based high-order-low-order representation of the transport equations. Due to the high dimensionality, memory is always a concern in simulating TRT. We form error estimates and adaptivity in time purely based on temperature and radiation energy, for a trivial overhead in computational cost and memory usage compared with the base second order integrators. We then test the adaptivity in time on the tophat and Larsen problem, demonstrating the ability of the adaptive algorithm to naturally vary the timestep across 4–5 orders of magnitude, ranging from the dynamical timescales of the streaming regime to the thick diffusion limit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Quantum Machine Learning in High Energy Physics

Machine learning has been used in high energy physics for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early 1980s as way to perform computations that would not be tractable with a classical computer. With the advent of noisy intermediate-scale quantum computing devices, more quantum algorithms are being developed with the aim at exploiting the capacity of the hardware for machine learning applications. An interesting question is whether there are ways to apply quantum machine learning to High Energy Physics. This paper reviews the first generation of ideas that use quantum machine learning on problems in high energy physics and provide an outlook on future applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Improving probabilistic infectious disease forecasting through coherence

With an estimated $10.4 billion in medical costs and 31.4 million outpatient visits each year, influenza poses a serious burden of disease in the United States. To provide insights and advance warning into the spread of influenza, the U.S. Centers for Disease Control and Prevention (CDC) runs a challenge for forecasting weighted influenza-like illness (wILI) at the national and regional level. Many models produce independent forecasts for each geographical unit, ignoring the constraint that the national wILI is a weighted sum of regional wILI, where the weights correspond to the population size of the region. We propose a novel algorithm that transforms a set of independent forecast distributions to obey this constraint, which we refer to as probabilistically coherent. Enforcing probabilistic coherence led to an increase in forecast skill for 79% of the models we tested over multiple flu seasons, highlighting the importance of respecting the forecasting system’s geographical hierarchy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Comparative Analysis of TRGBs (CATs) from Unsupervised, Multi-halo-field Measurements: Contrast is Key

The tip of the red giant branch (TRGB) is an apparent discontinuity of the luminosity function (LF) due to the end of the red giant evolutionary phase and is used to measure distances in the local universe. In practice, tip localization via edge detection response (EDR) relies on several methods applied on a case-by-case basis. It is hard to evaluate how individual choices affect a distance estimation using only a single host field while also avoiding confirmation bias. To devise a standardized approach, we compare unsupervised, algorithmic analyses of the TRGB in multiple halo fields per galaxy. We first optimize methods for the lowest field-to-field dispersion, including spatial filtering, smoothing, and weighting of LF, color band selection, and tip selection based on the number of likely RGB stars and the ratio of stars below versus above the tip (R). We find R, which we call the tip contrast, to be the most important indicator of the quality of EDR measurements; higher R selection can decrease field-to-field dispersion. Further, since R is found to correlate with the age or metallicity of the stellar population based on theoretical modeling, it might result in a displacement of the detected tip magnitude. We find a tip-contrast relation with a slope of -0.023 ± 0.0046 mag/ratio, an ~5σ result that can be used to correct these variations in the detections. When using TRGB to establish a distance ladder, consistent TRGB standardization using tip-contrast relation across rungs is vital to make robust cosmological measurements.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Partitioning Quantum Chemistry Simulations with Clifford Circuits

Current quantum computing hardware is restricted by the availability of only few, noisy qubits which limits the investigation of larger, more complex molecules in quantum chemistry calculations on quantum computers in the near term. Here, in this work, we investigate the limits of their classical and near-classical treatment while staying within the framework of quantum circuits and the variational quantum eigensolver. To this end, we consider naive and physically motivated, classically efficient product ansatz for the parametrized wavefunction adapting the separable-pair ansatz form. We combine it with post-treatment to account for interactions between subsystems originating from this ansatz. The classical treatment is given by another quantum circuit that has support between the enforced subsystems and is folded into the Hamiltonian. To avoid an exponential increase in the number of Hamiltonian terms, the entangling operations are constructed from purely Clifford or near-Clifford circuits. While Clifford circuits can be simulated efficiently classically, they are not universal. In order to account for missing expressibility, near-Clifford circuits with only few, selected non-Clifford gates are employed. The exact circuit structure to achieve this objective is molecule-dependent and is constructed using simulated annealing and genetic algorithms. We demonstrate our approach on a set of molecules of interest and investigate the extent of our methodology’s reach.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Analysis of Polarized Dust Emission Using Data from the First Flight of SPIDER

Using data from the first flight of Spider and from the Planck High Frequency Instrument, we probe the properties of polarized emission from interstellar dust in the Spider observing region. Component-separation algorithms operating in both the spatial and harmonic domains are applied to probe their consistency and to quantify modeling errors associated with their assumptions. Analyses of diffuse Galactic dust emission spanning the full Spider region demonstrate (i) a spectral energy distribution that is broadly consistent with a modified-blackbody (MBB) model with a spectral index of β d = 1.45 ± 0.05 (1.47 ± 0.06) for E (B)-mode polarization, slightly lower than that reported by Planck for the full sky; (ii) an angular power spectrum broadly consistent with a power law; and (iii) no significant detection of line-of-sight polarization decorrelation. Tests of several modeling uncertainties find only a modest impact (~10% in σ r ) on Spider's sensitivity to the cosmological tensor-to-scalar ratio. The size of the Spider region further allows for a statistically meaningful analysis of the variation in foreground properties within it. Assuming a fixed dust temperature T d = 19.6 K, an analysis of two independent subregions of that field results in inferred values of β d = 1.52 ± 0.06 and β d = 1.09 ± 0.09, which are inconsistent at the 3.9σ level. Furthermore, a joint analysis of Spider and Planck 217 and 353 GHz data within one subregion is inconsistent with a simple MBB at more than 3σ, assuming a common morphology of polarized dust emission over the full range of frequencies. This evidence of variation may inform the component-separation approaches of future cosmic microwave background polarization experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗
DOE OSTI2023

Low-Cost X-Ray CT System for Imaging of Roots

The goal of this project was to develop and demonstrate an innovative, low cost, field deployable, stationary 3D x-ray computed tomography (CT) system that will image total root phenotypes with a micron size resolution at a throughput of hundreds of plants per cycle. This system is based on UHV’s unique low cost linear x-ray tube technology and sophisticated reconstruction & image segmentation algorithms developed at University of Massachusetts, Lowel and University of Nottingham; and was tested for several types of soils at University of Wisconsin and Texas A&M University. Currently, no technologies exist that have been designed to image roots in complex media such as agricultural field conditions. Due to its small size, high resolution & fast imaging of fine roots, low power consumption, large penetration depth (i.e. ability to see through several feet of soil) and ease of field deployability, this CT system will increase the speed and efficacy of discovery, field translation, and deployment of improved crops and systems that improve soil carbon accumulation and storage, decrease N2O emissions, and improve water efficiency leading towards advancements that could mitigate 10% of the total US Greenhouse gases. This degree of imaging in the field has never been available and would be invaluable to scientists in understanding how environmental conditions and phenotypic variations contribute to carbon deposition through root development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes

Uncertainty propagation in complex engineering systems often poses significant computational challenges related to modeling and quantifying probability distributions of model outputs, as those emerge as the result of various sources of uncertainty that are inherent in the system under investigation. Gaussian Processes regression (GPs) is a robust meta-modeling technique that allows for fast model prediction and exploration of response surfaces. Multi-fidelity variations of GPs further leverage information from cheap and low fidelity model simulations in order to improve their predictive performance on the high fidelity model. In order to cope with the high volume of data required to train GPs in high dimensional design spaces, a common practice is to introduce latent design variables that are typically projections of the original input space to a lower dimensional subspace, and therefore substitute the problem of learning the initial high dimensional mapping, with that of training a GP on a low dimensional space. Here in this paper, we present a Bayesian approach to identify optimal transformations that map the input points to low dimensional latent variables. The \projection" mapping consists of an orthonormal matrix that is considered a priori unknown and needs to be inferred jointly with the GP parameters, conditioned on the available training data. The proposed Bayesian inference scheme relies on a two-step iterative algorithm that samples from the marginal posteriors of the GP parameters and the projection matrix respectively, both using Markov Chain Monte Carlo (MCMC) sampling. In order to take into account the orthogonality constraints imposed on the orthonormal projection matrix, a Geodesic Monte Carlo sampling algorithm is employed, that is suitable for exploiting probability measures on manifolds. We extend the proposed framework to multi-fidelity models using GPs including the scenarios of training multiple outputs together. We validate our framework on three synthetic problems with a known lower-dimensional subspace. The benefits of our proposed framework, are illustrated on the computationally challenging aerodynamic optimization of a last-stage blade for an industrial gas turbine, where we study the effect of an 85-dimensional shape parameterization of a three-dimensional airfoil on two output quantities of interest, specifically on the aerodynamic efficiency and the degree of reaction

42 ENGINEERING↗
DOE OSTI2021

Barren Plateaus Preclude Learning Scramblers

Scrambling processes, which rapidly spread entanglement through many-body quantum systems, are difficult to investigate using standard techniques, but are relevant to quantum chaos and thermalization. In this Letter, we ask if quantum machine learning (QML) could be used to investigate such processes. In this study we prove a no-go theorem for learning an unknown scrambling process with QML, showing that it is highly probable for any variational Ansatz to have a barren plateau landscape, i.e., cost gradients that vanish exponentially in the system size. This implies that the required resources scale exponentially even when strategies to avoid such scaling (e.g., from Ansatz -based barren plateaus or no-free-lunch theorems) are employed. Furthermore, we numerically and analytically extend our results to approximate scramblers. Hence, our work places generic limits on the learnability of unitaries when lacking prior information.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Responses of summer mesoscale convective systems to irrigation over the North China Plain based on convection-permitting model simulations

Extensive irrigation activities in the North China Plain (NCP) significantly influence regional weather and climate. However, previous studies focusing on the NCP were primarily based on coarse-resolution models, which are unable to explicitly resolve convection systems, causing large uncertainty in precipitation simulations. In this study, a convection-permitting model coupled with a dynamic irrigation scheme is utilized to investigate the impacts of irrigation on summertime mesoscale convective systems (MCSs) over the NCP. Sensitivity experiments with irrigation off and on are conducted for 5 summers and an MCS identification and tracking algorithm is applied to both satellite observations and model simulations. We find that incorporating irrigation in the model increases MCS precipitation, which agrees more with observations. The probability distributions of MCS lifetime, area, propagation speed, and intensity are all better simulated with irrigation. Irrigation increases the occurrence frequency of MCSs throughout the entire day. The nighttime increase is partly because of more frequent local initiation of MCS developed from isolated deep convection, while the daytime increase is mainly attributed to the changes in MCSs initiating elsewhere and then propagating to the NCP. On average, irrigation induces additional moisture that is more thermodynamically favorable for precipitation, but this effect is partially offset by the weakened ascending air motion primarily caused by irrigation surface cooling. Compared to weak MCS precipitation events, strong MCS precipitation events experience greater enhancement in precipitation intensity when including irrigation because the offset effect from the change in large-scale ascending air motion is insignificant. In addition, irrigation makes the variation of MCS precipitation intensity more correlated with the variation in ascending motion but less correlated with that in atmospheric moisture. Our results suggest the pronounced impacts of irrigation on MCSs over the NCP which should be included in numerical models to improve regional precipitation simulation and prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Modeling noise in global Mølmer-Sørensen interactions applied to quantum approximate optimization

Many-qubit Mølmer-Sørensen (MS) interactions applied to trapped ions offer unique capabilities for quantum information processing, with applications including quantum simulation and the quantum approximate optimization algorithm (QAOA). Here, we develop a physical model to describe many-qubit MS interactions under four sources of experimental noise: vibrational mode frequency fluctuations, laser power fluctuations, thermal initial vibrational states, and state preparation and measurement errors. The model parametrizes these errors from simple experimental measurements, without free parameters. Herein, we validate the model in comparison with experiments that implement sequences of MS interactions on two 171 Yb + ions. The model shows reasonable agreement after several MS interactions as quantified by the reduced chi-squared statistic $χ^{2}_{red}$≈2. As an application we examine MaxCut QAOA experiments on three and six ions. The experimental performance is quantified by approximation ratios that are 91% and 83% of the optimal theoretical values. Our model predicts $0.93^{+0.03}_{-0.02}$ and $0.95^{+0.04}_{-0.03}$, respectively, with disagreement in the latter value attributable to secondary noise sources beyond those considered in our analysis. With realistic experimental improvements to reduce measurement error and radial trap frequency variations, the model achieves approximation ratios that are 99% of the optimal. Incorporating these improvements into future experiments is expected to reveal new aspects of noise for future modeling and experimental improvements.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Effect of injected flux and current temporal phasing on self-organization in the HIT-SI3 experiment

The HIT-SI3 device at the University of Washington uses three oscillating inductive helicity injectors to form and sustain spheromak plasma equilibria. By adjusting the temporal phase of the injector waveforms with respect to each other, the toroidal spectrum of the imposed perturbations can be controlled. Using a recently implemented GPU-based control system, the available mode spectra were explored experimentally by scanning the space of relative injector phasing. In this space, significant variation in the toroidal mode spectrum ($n$ = 1, 2, 3) of the perturbations was observed. Additionally, variation in characteristics of driven equilibria was also observed, including a ≈ $30$% range in toroidal current gain ($I$ $Φ$ / $I$ $Inj$ ). Experimental results are compared with both a composite-equilibria and nonlinear dynamic model, including extended MHD simulations using the NIMROD code and composite Taylor state equilibria computed using the PSI-Tet code. In conclusion, qualitative agreement is seen with the nonlinear models, but not with composite-equilibria models, suggesting the use of nonlinear models to better capture observed plasma dynamics and provide predictive use for future experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Noisy-Intermediate-Scale Quantum Electromagnetic Transients Program

Quantum-empowered electromagnetic transients program (QEMTP) is a promising paradigm for tackling EMTP's computational burdens. Nevertheless, no existing studies truly achieve a practical and scalable QEMTP operable on today's noisy-intermediate-scale quantum (NISQ) computers. The strong reliance on noise-free and fault-tolerant quantum devices--which appears to be decades away--hinder practical applications of current QEMTP methods. Here, we devise a NISQ-QEMTP methodology which for the first time transitions the QEMTP operations from ideal, noise-free quantum simulators to real, noisy quantum computers. The main contributions lie in: (1) a shallow-depth QEMTP quantum circuit for mitigating noises on NISQ quantum devices; (2) practical QEMTP linear solvers incorporating executable quantum state preparation and measurements for nodal voltage computations; (3) a noise-resilient QEMTP algorithm leveraging quantum resources logarithmically scaled with power system dimension; (4) a quantum shifted frequency analysis (QSFA) for accelerating QEMTP by exploiting dynamic phasor simulations with larger time steps; (5) a systematical analysis on QEMTPs performance under various noisy quantum environments. Extensive experiments systematically verify the accuracy, efficacy, universality and noise-resilience of QEMTP on both noise-free simulators and IBM real quantum computers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Electronic structure theory with molecular point group symmetries on quantum annealers

Quantum computation has the potential to revolutionize quantum chemistry through major speedups in computation times and an exponential reduction in computational resources. Here, we combine the symmetry-adapted Jordan–Wigner encoding based on the full Boolean symmetry group $\mathbb{Z}$$^{k}_{2}$ with our new implementation of the Xia–Bian–Kais (XBK) method for improving the efficiency of electronic structure theory calculations on quantum annealers, particularly by reducing the number of qubits needed to achieve the same accuracy. By providing a more extensive symmetry-adapted encoding (SAE) than previous work, we are able to simulate molecules larger than those previously reported that have been studied using methods developed for quantum annealers and without using an active space. We calculated the potential energy surfaces of H 2 , LiH, He 2 , H 2 O, O 2 , N 2 , Li 2 , F 2 , CO, BH 3 , NH 3 , and CH 4 , with the largest molecule in the STO-6G basis set requiring 16 qubits with our SAE, and compared them with full configuration interaction results. The application of SAE to the XBK method provides an exponential reduction in the size of the Hilbert space and scales well with the size of the problem. It does not introduce significant additional errors for even or large values of a key variational parameter that determines the number of ancilla qubits used in the XBK method’s Hamiltonian embedding, or for certain molecules such as He 2 and H 2 O. Here, we provide an explanation for this behavior and a recommendation on the usage of our method. In addition, we briefly discuss the potential of extracting electronic excited states from our method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

ARM shortwave spectrometers to study the clear-cloud transition zone and mixing processes

The proposed research is a collaborative effort between NASA/Goddard Space Flight Center and the Hebrew University of Jerusalem. While the NASA team focused on analyzing ground-based hyper-spectral radiance observations to understand cloud edge properties and their connection to mixing processes, the Hebrew University team tackled the problem through cloud modeling activities. By approximating the shortwave spectra in the cloud-clear transition zone as a linear combination of purely clear and purely cloudy spectra we can characterize the variations of cloud optical thickness and cloud droplet effective radius in the transition zone. When applying this method to the measurements of a ground-based shortwave spectroradiometer at the ARM’s SGP site, representing continental conditions, and MAGIC field campaign between Log Angeles, California and Honolulu, Hawaii, representing maritime scenarios, we found that cloud optical depth consistently decreases in both cases, but droplet size decreases much more substantially for the continental regime, suggesting different mixing processes for the continental and maritime conditions. The investigation and measurements of radiation clouds were coupled with a unique cloud modeling. A novel spectral bin microphysics was developed and implemented to the System of Atmospheric Modeling (SAM). In order to resolve cloud transition zones with high spatial gradients of microphysical variables a unique high resolution (10 m) was used in simulations. The model calculates droplet size distributions in each grid point. The model output was transferred to the NASA/GSFC team for utilization in radiative calculations and testing of both radiative algorithm and model representation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Magnetism in metastable and annealed compositionally complex alloys

Compositionally complex materials (CCMs) present a potential paradigm shift in the design of magnetic materials. These alloys exhibit long-range structural order coupled with limited or no chemical order. As a result, extreme local environments exist with a large variations in the magnetic energy terms, which can manifest large changes in the magnetic behavior. In the current work, the magnetic properties of (Cr, Mn, Fe, Ni) alloys are presented. These materials were prepared by room-temperature combinatorial sputtering, resulting in a range of compositions with a single bcc structural phase and no chemical ordering. The combinatorial growth technique allows CCMs to be prepared outside of their thermodynamically stable phase, enabling the exploration of otherwise inaccessible order. The mixed ferromagnetic and antiferromagnetic interactions in these alloys causes frustrated magnetic behavior, which results in an extremely low coercivity (<1mT), which increases rapidly at 50 K. At low temperatures, the coercivity achieves values of nearly 500 mT, which is comparable to some high-anisotropy magnetic materials. Further, commensurate with the divergent coercivity is an atypical drop in the temperature dependent magnetization. These effects are explained by a mixed magnetic phase model, consisting of ferro-, antiferro-, and frustrated magnetic regions, and are rationalized by simulations. A machine-learning algorithm is employed to visualize the parameter space and inform the development of subsequent compositions. Annealing the samples at 600 °C orders the sample, more-than doubling the Curie temperature and increasing the saturation magnetization by as much as 5×. Simultaneously, the large coercivities are suppressed, resulting in magnetic behavior that is largely temperature independent over a range of 350 K. The ability to transform from a hard magnet to a soft magnet over a narrow temperature range makes these materials promising for heat-assisted recording technologies.

36 MATERIALS SCIENCE↗