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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Topological Materials for Functional Optoelectronic Devices

The recent realization of topology as a mathematical concept in condensed matter systems has shattered Landau's widely accepted classification of phases by spontaneous symmetry breaking as he famously said, “a particular symmetry property exists or does not exist.” Topological materials (TMs) such as topological insulators and topological semimetals, are characterized by properties that depend on the topology of the band structure. Such dependence has drastic implications on the optical, electrical, and thermal properties of the material. Fundamental physics of TMs is currently under active research in condensed matter, materials science, and high energy physics. Here, recent advances in exploiting the unique properties of TMs to realize functional optoelectronic devices are surveyed. Current and future applications that are, or may be, enabled by their unique properties are discussed. Although many theoretical ideas have been proposed over the past decade or so on using TMs in optoelectronic applications, the focus will be on experimentally realized devices.

36 MATERIALS SCIENCE↗
DOE OSTI2024

JARVIS-Leaderboard: a large scale benchmark of materials design methods

Abstract Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/

36 MATERIALS SCIENCE↗
DOE OSTI2023

Learning and Predicting Photonic Responses of Plasmonic Nanoparticle Assemblies via Dual Variational Autoencoders

In this work, the application of machine learning is demonstrated for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational autoencoder (dual-VAE). In this approach, the information is shared between the latent spaces of two VAEs acting on the particle shape data and spectral data, respectively, but enforcing a common encoding on the shape-spectra pairs. It is shown that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that hyperspectral darkfield microscopy can be used to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58). This approach of building structure-property relationships via shared encoding is universal and should have applications to a broader range of materials science and physics problems in imaging of both molecular and nanomaterial systems.

variational autoencoder↗
DOE OSTI2024

Synthesis and growth of solution-processed chiral perovskites

In materials science, chiral perovskites stand out due to their exceptional optoelectronic properties and the versatility in their structure and composition, positioning them as crucial in the advances of technologies in spintronics and chiroptical systems. This review underlines the critical role of synthesizing and growing these materials, a process integral to leveraging their complex interplay between structural chirality and distinctive optoelectronic properties, including chiral-induced spin selectivity and chiroptical activity. The paper offers a comprehensive summary and discussion of the methods used in the synthesis and growth of chiral perovskites, delving into extensive growth techniques, fundamental mechanisms, and strategic approaches for the engineering of low-dimensional perovskites, alongside the creation of novel chiral ligands. The necessity of developing new synthetic approaches and maintaining precise control during the growth of chiral perovskites is emphasized, aiming to enhance their structural chirality and boost their efficiency in spin and chiroptical selectivity.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Growth of New High-Quality and Large-Size Single Crystals via High Pressure Floating-Zone Technique

This award supports experimental materials physics research on a class of technologically relevant materials that cannot form under ambient conditions. With the complementary expertise at Louisiana State University (LSU) and infrastructure at Oak Ridge National Laboratory (ORNL), single crystals of these materials will be synthesized using the high-pressure floating-zone technique. Our ability to grow high-quality single crystals will facilitate advanced characterization of these materials and improve the scientific outcomes. In addition to in-house material characterization, the principle investigator will partner with scientists at ORNL and Brookhaven National Laboratory (BNL) to tackle materials problems using the state-of-the-art characterization techniques. This will allow us to explore the fundamental physics underlying the emergent phenomena only seen in materials prepared under extreme conditions. Our ultimate goal is to address one of the grand challenges of materials science: how do complex phenomena emerge from simple ingredients? The proposed project will also spearhead a unique opportunity to train “new breed” scientists, co-advised by experts at both LSU and U. S. Department of Energy National Laboratories. They will be essential for expanding materials physics research with ability to grow crystals, characterize their properties, and design new materials -- an imperative skill that is scarce in the U. S. scientific community.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Pressure-Induced Phase Transformations

The study of phase transitions in solids under high pressure conditions is a very active and vigorous research field. In recent decades, thanks to the development of experimental techniques and computer simulations, a plethora of important discoveries has been made under high-pressure conditions. Many of the achievements accomplished in recent years a affect various research fields, from solid-state physics to chemistry, materials science, and geophysics. They not only contribute to a deeper understanding of solid-sold phase transitions but also to a better understanding of melting under compression.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Nucleation, growth, and superlattice formation of nanocrystals observed in liquid cell transmission electron microscopy

This work reviews the advancements and prospects of liquid cell transmission electron microscopy (TEM) imaging and analysis methods in understanding the nucleation, growth, etching, and assembly dynamics of nanocrystals. The bonding of atoms into nanoscale crystallites produces materials with nonadditive properties unique to their size and geometry. The recent application of in situ liquid cell TEM to nanocrystal development has initiated a paradigm shift, (1) from trial-and-error synthesis to a mechanistic understanding of the “synthetic reactions” responsible for the emergence of crystallites from a disordered soup of reactive species (e.g., ions, atoms, molecules) and shape-defined growth or etching; and (2) from post-processing characterization of the nanocrystals’ superlattice assemblies to in situ imaging and mapping of the fundamental interactions and energy landscape governing their collective phase behaviors. Imaging nanocrystal formation and assembly processes on the single-particle level in solution immediately impacts many existing fields, including materials science, nanochemistry, colloidal science, biology, environmental science, electrochemistry, mineralization, soft condensed-matter physics, and device fabrication.

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 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 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 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 OSTI2025

QUCODE: End-to-End Qubit Co-Design

The design of a quantum computer can be broken down into different steps, e.g., the material science aspect of designing qubits and devices, considerations of controlling the state of the qubits and their environment, the computer science aspects of mapping algorithms to the available primitives of the quantum computer, and the programming of an application in terms of the available algorithms. Research in these areas is currently fairly isolated, and there is framework for an end-to-end design approach where a desired application informs the choice of materials for the qubits and their environment, and vice versa.We identify knowledge gaps and opportunities for research that builds on existing PNNL capabilities.

36 MATERIALS SCIENCE↗