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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Chemical Complexity Controls Energy Dissipation and Defect Evolution

The development of metallic alloys is arguably one of the oldest sciences, dating back at least 3,000 years. Most research and applications have been focused on alloys with comprised multiple phases with one or two dominant elements, to which the addition of alloying elements in low concentrations leads to various performance improvements and changes in radiation resistance. In sharp contrast to traditional alloys, recent success in the synthesis of single phase concentrated solid solution alloys (SP-CSAs) has opened up new frontiers in materials research. In these alloys, a random arrangement of multiple elemental species on a lattice (fcc or bcc) results in unique site-to-site lattice distortions and local disordered chemical environments. Intense radiation in nuclear fission and fusion energy power systems, nuclear waste forms, high-energy accelerators and space exploration transfers energy to the electrons and atoms that make up the material, and thereby produces defects that ultimately compromise material strength and lifetime. A grand challenge in materials research is to understand complex electronic correlations and non-equilibrium atomic interactions, and how such intrinsic properties and dynamic processes affect energy transfer and defect evolution in irradiated materials. Since SP-CSAs possess unique links between intrinsic material properties (can be altered by alloy complexity), energy dissipation and various defect dynamic processes; they are ideal systems to fill knowledge gaps between electronic- /atomic-level interactions and radiation resistance mechanisms. In our work, we show that chemical disorder and compositional complexity in SP-CSAs have an enormous impact on defect dynamics through substantial modification of energy dissipation pathways. Based on a closely integrated computational and experimental study using a novel set of Ni-based SP-CSAs, we have explicitly demonstrated that increasing chemical disorder can lead to a substantial reduction in the electron mean free path and electrical and thermal conductivity. These reductions have a significant impact on energy dissipation and consequentially on defect evolution during ion irradiation. Considerable enhancement in radiation resistance with increasing chemical complexity from pure nickel to binary, and to more complex quaternary solid solutions, is observed under ion irradiation. Contrary to conventional alloys with low solute concentration but multiple phases, energy dissipation and defect evolution at the level of electrons and atoms in SP- CSA systems with extreme compositional disorder is an unexplored frontier in materials science. The integrated experimental/modeling effort provides new insights into defect dynamics at the level of atoms and electrons, and an innovative path forward towards solving a long-standing challenge in structural materials. Increasing chemical complexity achieved orders-of-magnitude reductions in electron mean free paths and electron and thermal conductivities of alloys of NiCo, NiFe and NiCoFeCr, compared to Ni, and suppressed damage accumulation under ion irradiation. This discovery provides insights to address the grand challenge of understanding complex electronic correlations and non-equilibrium atomic interactions, and the effect of these intrinsic properties and dynamic processes on energy transfer and defect evolution in irradiated materials. Understanding how material properties and defect dynamics can be tailored by alloy complexity may pave the way for new design principles of functional alloys. (authors)

36 MATERIALS SCIENCE↗
DOE OSTI2024

Performance analysis of photodetectors based on 2D materials and heterostructures

The unprecedented demand for sophisticated, self-powered, compact, ultrafast, cost-effective, and broadband light sensors for a myriad of applications has spurred a lot of research, precipitating in a slew of studies over the last decade. Apart from the photosensing ability of an active element in the light sensor, the device architecture is crucial in terms of photoinduced charge carrier generation and separation. Since the inception of graphene and the subsequent research growth in the atomically thin 2D materials, researchers have developed and adapted different families of 2D materials and device architectures, including single element 2D, 0D/2D, 2D/2D, 1D/2D stacked structures, and so on. Here this review discusses the recent reports on the light-sensing properties of various 2D materials, their heterostructures, and characteristics applicable to the ultraviolet-near infrared (UV-NIR), short-wave IR (SWIR), mid-wave IR (MWIR), long-wave IR (LWIR), and terahertz (THz) spectral ranges. It highlights the novelty of the burgeoning field, the heightened activity at the boundaries of engineering and materials science, particularly in the generation of charge carriers, their separation, and extraction, and the increased understanding of the underpinning science through modern experimental approaches. Devices based on the simultaneous effects of the pyro-phototronic effect (PPE) and the localized surface plasmon resonance (LSPR) effect, the photothermoelectric effect (PTE)-assisted photodetectors (PDs), waveguide-integrated silicon-2D PDs, metal-2D-metal PDs, and organic material PDs are examined rigorously. Theoretical treatment utilizing various computational approaches to investigate 2D materials and heterostructures for photodetection applications is also briefly discussed. At the end, current challenges and solutions to enhance the figures of merit of photodetectors are proposed.

2D transition metal chalcogenides↗
DOE OSTI2024

Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets

Abstract Modern data mining methods have demonstrated effectiveness in comprehending and predicting materials properties. An essential component in the process of materials discovery is to know which material(s) will possess desirable properties. For many materials properties, performing experiments and density functional theory computations are costly and time-consuming. Hence, it is challenging to build accurate predictive models for such properties using conventional data mining methods due to the small amount of available data. Here we present a framework for materials property prediction tasks using structure information that leverages graph neural network-based architecture along with deep-transfer-learning techniques to drastically improve the model’s predictive ability on diverse materials (3D/2D, inorganic/organic, computational/experimental) data. We evaluated the proposed framework in cross-property and cross-materials class scenarios using 115 datasets to find that transfer learning models outperform the models trained from scratch in 104 cases, i.e., ≈90%, with additional benefits in performance for extrapolation problems. We believe the proposed framework can be widely useful in accelerating materials discovery in materials science.

Chemistry↗
DOE OSTI2018

Surface-screening mechanisms in ferroelectric thin films and their effect on polarization dynamics and domain structures

For over 70 years, ferroelectric materials have been one of the central research topics for condensed matter physics and material science, an interest driven both by fundamental science and applications. However, ferroelectric surfaces, the key component of ferroelectric films and nanostructures, still present a significant theoretical and even conceptual challenge. Indeed, stability of ferroelectric phase per se necessitates screening of polarization charge. At surfaces, this can lead to coupling between ferroelectric and semiconducting properties of material, or with surface (electro) chemistry, going well beyond classical models applicable for ferroelectric interfaces. In this paper, we summarize recent studies of surface-screening phenomena in ferroelectrics. We provide a brief overview of the historical understanding of the physics of ferroelectric surfaces, and existing theoretical models that both introduce screening mechanisms and explore the relationship between screening and relevant aspects of ferroelectric functionalities starting from phase stability itself. Given that the majority of ferroelectrics exist in multiple-domain states, we focus on local studies of screening phenomena using scanning probe microscopy techniques. We discuss recent studies of static and dynamic phenomena on ferroelectric surfaces, as well as phenomena observed under lateral transport, light, chemical, and pressure stimuli. Finally, we also note that the need for ionic screening renders polarization switching a coupled physical–electrochemical process and discuss the non-trivial phenomena such as chaotic behavior during domain switching that stem from this.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Revealing Local Structures through Machine-Learning-Fused Multimodal Spectroscopy

Atomistic structures of materials offer valuable insights into their functionality. Determining these structures remains a fundamental challenge in materials science, especially for systems with defects. While both experimental and computational methods exist, each has limitations in resolving nanoscale structures. Core-level spectroscopies, such as X-ray absorption (XAS) or electron energy-loss spectroscopies (EELS), have been used to determine the local bonding environment and structure of materials. Recently, machine learning (ML) methods have been applied to extract structural and bonding information from XAS/EELS data. However, frameworks relying solely on a single data stream, defined as characterization data derived from a single element using one technique, are often insufficient because multiple local environments can yield similar spectral features, making it challenging to differentiate between competing structural hypotheses. Here, in this work, we address this challenge by integrating multimodal ab initio simulations, experimental data acquisition, and ML techniques for structure characterization. Our goal is to determine local structures and properties using EELS and XAS data from multiple elements and edges. To showcase our approach, we use various lithium nickel manganese cobalt (NMC) oxide compounds which are used for lithium ion batteries, including those with oxygen vacancies and antisite defects, as the sample material system. We successfully inferred local element content, ranging from lithium to transition metals, with quantitative agreement with experimental data. Beyond local element inference, we find that ML model based on multimodal spectroscopic data is able to determine whether local defects such as oxygen vacancy and antisites are present, a task which is impossible for single mode spectra or other experimental techniques. Furthermore, our framework is able to provide physical interpretability, bridging spectroscopy with the local atomic and electronic structures.

battery↗
DOE OSTI2025

Accurate and uncertainty-aware multi-task prediction of HEA properties using prior-guided deep Gaussian processes

Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys (HEAs), especially when integrating computational predictions with sparse experimental observations. This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes (cGP), Deep Gaussian processes (DGP), encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting (XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V. We specifically assess their capabilities in predicting correlated material properties, including yield strength, hardness, modulus, ultimate tensile strength, elongation, and average hardness under dynamic/quasi-static conditions, alongside auxiliary computational properties. The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic, heterotopic, and incomplete data commonly encountered in materials science. Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperform other surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge. This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and data-efficient materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Studying light–matter interactions and energy transfer at the nanoscale with a trapped–ion quantum computer

A grand challenge in materials sciences is to control energy transduction, transfer and dissipation at the nanoscale. Meeting this challenge requires progress in nanoscale fabrication and synthesis, but equally importantly, also progress in our understanding of how nanoscale structure influences these phenomena. Although detailed quantum mechanical models exist that capture the structure and physics of materials and molecular species, solving these models to calculate the relevant emergent phenomena, which often occur over vastly different time and energy scales, has so far proven difficult. In this project, we studied how to model energy transfer and light-matter interactions using trapped-ion quantum computers. Our studies focused on emulation of critical processes in natural systems, such as the well-known photosynthetic pigment-protein complexes responsible for capturing and transducing light to electrons. In addition, we implemented proof-of-principle experiments demonstrating that such calculations are possible on trapped-ion quantum computing hardware. These studies and experiments pave the way to investigate energy transport and transduction in a detail not accessible with classical computing technologies. We expect that our studies will allow to extract important guiding principles on how to improve the efficiency of nano-scale light-harvesting and transduction devices.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Feasibility of Using Steel Drums for the Confinement of Burned Low-Level Radioactive Waste Ashes

The selection of suitable materials to construct a facility for the final disposal of radioactive wastes (repository) is a challenge in materials science, since these facilities must be designed to provide confinement to the radionuclides for long periods of time. In the case of the low-level radioactive waste (LLRW) repository, the confinement of the species must be guaranteed for more than 300 years. In this context, the evaluation of the proposed materials must be performed under accelerated laboratory tests. One type of LLRW consists of ashes that result from the incineration of solid combustible radioactive waste. In Argentina, one of the proposed projects is to place these ashes inside steel drums and immobilized them by a cementation process. The issue to be resolved is whether the steel drums will undergo internal corrosion depending on the presence of certain deleterious species. The aim of the present work is to evaluate the corrosion susceptibility of steel drums containing simulated cemented incineration ashes. To this purpose, a special type of specimen was designed and the corrosion process was followed up for almost 4 years. It was found that, although the deleterious effect of chloride is undeniable, corrosion rates are not as high as to compromise the integrity of the steel drums for a design period of 300 years.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Dislocation density evolution of AA 7020-T6 investigated by in-situ synchrotron diffraction under tensile load

The dislocation density evolution along the loading axis of a textured AA 7020-T6 aluminum alloy during uniaxial tension was investigated by in-situ synchrotron diffraction. The highly parallel synchrotron beam at the High Energy Materials Science beamline P07 in PETRA III, DESY, offers excellent conditions to separate different influences for line broadening from which micro-strains are obtained using the modified Williamson–Hall method which is also for defect density investigations. During tensile loading the dislocation density evolution was documented from the as-received material (initial micro-strain state) to the relaxation of the strains during elastic deformation. After yield, the increasing rate of dislocation density growth was relatively fast till half-way between yield and UTS. After that, the rate started to decrease and the dislocation density fluctuated as the elongation increased due to the generation and annihilation of dislocations. When dislocation generation is dominant, the correlation between the flow stress and dislocation density satisfies the Taylor equation. Besides, a method to correct the thickness effect on peak broadening is developed in the present study. - Highlights: • In-situ synchrotron diffraction was applied to characterize peak broadening. • Dislocation evolution along the loading axis during uniaxial tension was investigated. • A method to correct the sample thickness effect on peak broadening was developed. • Dislocation density and flow stress satisfy the Taylor equation at a certain range. • The texture before load and after sample fracture was analyzed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

MENUS—Materials engineering by neutron scattering

Materials engineering by neutron scattering (MENUS) at the second target station will be a transformational high-flux, versatile, multiscale materials engineering diffraction beamline with unprecedented new capabilities for the study of complex materials and structures. It will support both fundamental and applied materials research in a broad range of fields. MENUS will combine unprecedented long-wavelength neutron flux and unique detector coverage to enable real-time studies of complex structural and functional materials under external stimuli. The incorporated small angle neutron scattering and transmission/imaging capabilities will extend its sensitivity to larger length scales and higher spatial resolution. Multimodal MENUS will provide crystallographic and microstructure data to the materials science and engineering community to understand lattice strain/phase transition/microstructure/texture evolution in three orthogonal directions in complex material systems under combined extreme applied conditions. Finally, the capabilities of MENUS will open new scientific opportunities and meet the research needs for science challenges to enable studies of a range of phenomena and answer the key questions in material design/exploration, advanced material processing, transformative manufacturing, and material operations of national impacts in our daily life.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

ZENN: A thermodynamics-inspired computational framework for heterogeneous data–driven modeling

Traditional entropy-based methods—such as cross-entropy loss in classification problems—have long been essential tools for representing the information uncertainty and physical disorder in data and for developing artificial intelligence algorithms. However, the rapid growth of data across various domains has introduced new challenges, particularly the integration of heterogeneous datasets with intrinsic disparities. To address this, we introduce a zentropy-enhanced neural network (ZENN), extending zentropy theory into the data science domain via intrinsic entropy, enabling more effective learning from heterogeneous data sources. ZENN simultaneously learns both energy and intrinsic entropy components, capturing the underlying structure of multisource data. To support this, we redesign the neural network architecture to better reflect the intrinsic properties and variability inherent in diverse datasets. We demonstrate the effectiveness of ZENN on classification tasks and energy landscape reconstructions, showing its superior generalization capabilities and robustness-particularly in predicting high-order derivatives. In image and text classification tasks, ZENN demonstrates superior generalization by introducing a learnable temperature variable that models latent multisource heterogeneity, allowing it to surpass state-of-the-art models on CIFAR-10/100, BBC News, and AG News. As a practical application in materials science, we employ ZENN to reconstruct the Helmholtz energy landscape of Fe3Pt using data generated from density functional theory and capture key material behaviors, including negative thermal expansion and the critical point in the temperature–pressure space. Overall, this work presents a zentropy-grounded framework for data-driven machine learning, positioning ZENN as a versatile and robust approach for scientific problems involving complex, heterogeneous datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Learning robust parameter inference and density reconstruction in flyer plate impact experiments

Estimating physical parameters or material properties from experimental observations is a common objective in many areas of physics and material science. In many experiments, especially in shock physics, radiography is the primary means of observing the system of interest. However, radiography does not provide direct access to key state variables, such as density, which prevents the application of traditional parameter estimation approaches. Here we focus on flyer plate impact experiments on porous materials, and resolving the underlying parameterized equation of state (EoS) and crush porosity model parameters given radiographic observation(s). We use machine learning as a tool to demonstrate with high confidence that using only high impact velocity data does not provide sufficient information to accurately infer both EoS and crush model parameters, even with fully resolved density fields or a dynamic sequence of images. We thus propose an observable data set consisting of low and high impact velocity experiments/simulations that capture different regimes of compaction and shock propagation, and proceed to introduce a generative machine learning approach which produces a posterior distribution of physical parameters directly from radiographs. We demonstrate the effectiveness of the approach in estimating parameters from simulated flyer plate impact experiments, and show that the obtained estimates of EoS and crush model parameters can then be used in hydrodynamic simulations to obtain accurate and physically admissible density reconstructions. Finally, we examine the robustness of the approach to model mismatches, and find that the learned approach can provide useful parameter estimates in the presence of out-of-distribution radiographic noise and previously unseen physics, thereby promoting a potential breakthrough in estimating material properties from experimental radiographic images.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia

Cost versus accuracy trade-offs are frequently encountered in materials science and engineering, where a particular property of interest can be measured/computed at different levels of accuracy or fidelity. Naturally, the most accurate measurement is also the most resource and time intensive, while the inexpensive quicker alternatives tend to be noisy. In such situations, a number of machine learning (ML) based multifidelity information fusion (MFIF) strategies can be employed to fuse information accessible from varying sources of fidelity and make predictions at the highest level of accuracy. In this work, we perform a comparative study on traditionally employed single-fidelity and three MFIF strategies, namely, (1) Δ-learning, (2) low-fidelity as a feature, and (3) multifidelity cokriging (CK) to compare their relative prediction accuracies and efficiencies for accelerated property predictions and high throughput chemical space explorations. We perform our analysis using a dopant formation energy data set for hafnia, which is a well-known high- k material and is being extensively studied for its promising ferroelectric, piezoelectric, and pyroelectric properties. We use a dopant formation energy data set of 42 dopants in hafnia—each studied in six different hafnia phases—computed at two levels of fidelities to find merits and limitations of these ML strategies. The findings of this work indicate that the MFIF based learning schemes outperform the traditional SF machine learning methods, such as Gaussian process regression and CK provides an accurate, inexpensive and flexible alternative to other MFIF strategies. While the results presented here are for the case study of hafnia, they are expected to be general. Furthermore, materials discovery problems that involve huge chemical space explorations can be studied efficiently (or even made feasible in some situations) through a combination of a large number of low-fidelity and a few high-fidelity measurements/computations, in conjunction with the CK approach.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Innovative Glazing Materials

Windows have the unique capability of being able to achieve a net zero energy impact by admitting solar gains in the winter to offset thermal losses and admitting daylight to offset electric lighting. If rejection or admission of solar heat gains and daylight are appropriately timed, then heating, cooling and lighting energy use at the perimeter zone can be reduced to net zero energy levels. Nano-scale switchable coatings on glass have been developed to actively modulate solar intensity and spectral transmission. We provide a brief overview of these switchable glazing materials, discuss the desired performance objectives for such materials, and present results from recently completed monitored studies of state-of-the-art switchable windows, particularly with respect to occupant response and market factors. Careful application of state-of-the-art switchable windows and new material science developments on the horizon can deliver the desired net zero energy performance while meeting critical human factors and market related requirements.

Building energy efficiency↗
DOE OSTI2021

Advances in atomistic modeling and understanding of drying shrinkage in cementitious materials

Despite more than a century of research, drying shrinkage still causes unwanted stresses and cracks in our concrete infrastructures. This is partly due to the lack of understanding of the underlying mechanisms governing the drying shrinkage in cementitious materials. Inspired by the tremendous impact of atomistic simulations on materials science, we expect such innovative simulation techniques to hold a key to unlocking the enigmatic nature of drying shrinkage at the nanoscale. In this treatise, we first introduce basic physics concepts and atomistic simulation methods in detail. Afterward, we discuss the progress brought by atomistic simulations in understanding the drying shrinkage in cementitious materials and other nanoporous and nanolayered systems. This review also highlights important remaining fundamental questions and practical issues regarding drying shrinkage and delineates how atomistic simulations can help resolve them.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Radiation-induced aging of PDMS Elastomer TR-55: a summary of constitutive, mesoscale, and population-based models

Filled and cross-linked elastomeric rubbers are versatile network materials with a multitude of applications ranging from artificial organs and biomedical devices to cushions, coatings, adhesives, interconnects, and seismic-isolation-, thermal-, and electrical barriers. External factors like mechanical stress, temperature fluctuations, or radiation are known to create chemical changes in such materials that can directly affect the molecular weight distribution (MWD) of the polymer between cross-links and alter the structural and mechanical properties. From a Materials Science point of view it is highly desirable to understand, effect, and manipulate such property changes in a controlled manner. In this report we summarize our modeling efforts on a polysiloxane elastomer TR-55, which is an important component in several of our systems, and representative of a wide class of filled rubber materials. The primary aging driver in this work has been γ-radiation, and a variety of modeling approaches have been employed, including constitutive, mesoscale, and population-based models. The work utilizes diverse experimental data, including mechanical stress-strain and compression set measurements, as well as MWD measurements using multiquantum NMR.

36 MATERIALS SCIENCE↗