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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Broadband antireflection and absorption enhancement of ultrathin silicon solar microcells enabled with density-graded surface nanostructures

Density-graded surface nanostructures are implemented on ultrathin silicon solar microcells by silver-nanoparticle-catalyzed wet chemical etching to enable near-zero surface reflection over a broad wavelength range of incident solar spectrum as well as non-zeroth order diffraction and light trapping for longer wavelength photons, thereby achieving augmented photon absorption for ultrathin silicon microcells in a simple, cost-effective manner. The increase of absorbed photon flux through the “black silicon (b-Si)” surface translates directly into the corresponding enhancement of photovoltaic performance, where 5.7-μm b-Si microcells with the rational design of device configuration exhibit improved energy conversion efficiency by 148% and 50% with and without a diffuse backside reflector, respectively, compared to devices from the bare silicon without b-Si implementation. Systematic studies on nanostructured morphology, optical and electrical properties of b-Si microcells, together with semi-empirical numerical modeling of photon absorption, provide key aspects of underlying materials science and physics.

14 SOLAR ENERGY↗
DOE OSTI2023

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Materials Science↗
DOE OSTI2021

Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data

Abstract Artificial intelligence (AI) and machine learning (ML) have been increasingly used in materials science to build predictive models and accelerate discovery. For selected properties, availability of large databases has also facilitated application of deep learning (DL) and transfer learning (TL). However, unavailability of large datasets for a majority of properties prohibits widespread application of DL/TL. We present a cross-property deep-transfer-learning framework that leverages models trained on large datasets to build models on small datasets of different properties. We test the proposed framework on 39 computational and two experimental datasets and find that the TL models with only elemental fractions as input outperform ML/DL models trained from scratch even when they are allowed to use physical attributes as input, for 27/39 (≈ 69%) computational and both the experimental datasets. We believe that the proposed framework can be widely useful to tackle the small data challenge in applying AI/ML in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Finding Electronic Structure Machine Learning Surrogates without Training

A myriad of phenomena in materials science and chemistry rely on quantum-level simulations of the electronic structure in matter. While moving to larger length and time scales has been a pressing issue for decades, such large-scale electronic structure calculations are still challenging despite modern software approaches and advances in high-performance computing. The silver lining in this regard is the use of machine learning to accelerate electronic structure calculations – this line of research has recently gained growing attention. The grand challenge therein is finding a suitable machine-learning model during a process called hyperparameter optimization. This, however, causes a massive computational overhead in addition to that of data generation. We accelerate the construction of machine-learning surrogate models by roughly two orders of magnitude by circumventing excessive training during the hyperparameter optimization phase. We demonstrate our workflow for Kohn-Sham density functional theory, the most popular computational method in materials science and chemistry.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Big Data: Next-Generation Machines for Big Science

Addressing the scientific grand challenges identified by the US Department of Energy’s (DOE’s) Office of Science’s programs alone demands a total leadership-class computing capability of 150 to 400 Pflops by the end of this decade. The successors to three of the DOE’s most powerful leadership-class machines are set to arrive in 2017 and 2018—the products of the Collaboration Oak Ridge Argonne Livermore (CORAL) initiative, a national laboratory–industry design/build approach to engineering nextgeneration petascale computers for grand challenge science. These mission-critical machines will enable discoveries in key scientific fields such as energy, biotechnology, nanotechnology, materials science, and high-performance computing, and serve as a milestone on the path to deploying exascale computing capabilities.

High-performance computing↗
DOE OSTI2015

Giant supercurrent states in a superconductor-InAs/GaSb-superconductor junction

Superconductivity in topological materials has attracted a great deal of interest in both electron physics and material sciences since the theoretical predictions that Majorana fermions can be realized in topological superconductors. Topological superconductivity could be realized in a type II, band-inverted, InAs/GaSb quantum well if it is in proximity to a conventional superconductor. Here, we report observations of the proximity effect induced giant supercurrent states in an InAs/GaSb bilayer system that is sandwiched between two superconducting tantalum electrodes to form a superconductor-InAs/GaSb-superconductor junction. Electron transport results show that the supercurrent states can be preserved in a surprisingly large temperature-magnetic field (T – H) parameter space. In addition, the evolution of differential resistance in T and H reveals an interesting superconducting gap structure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2018

Composite Bonded Joint: Repair Development

This report documents the details of my work as a NASA KSC intern for the Fall Session from August 27th to December 14th, 2018. My efforts and contributions were with the Materials Science Branch, a staffed organization within the Laboratories, Development, & Testing (NE-L) Division of the Engineering Directorate. The principle responsibilities of the Materials Science group are to support the design, development, and operations activities for materials and processes with the purpose of providing unique solutions for flight hardware, ground support equipment, and customer requests. My role as an intern focused on assisting engineers in developing repair processes to mature bonded joint technology in support of SLS ? scale hardware. My primary goals for this internship were to become more familiar with composite materials and learn more about processes I was unfamiliar with, such as prepregs and out-of-autoclave processing. This project allowed me to learn new skills such as scarfing and curing composites. Additional goals I had were to learn more about NASA's laboratories and projects under development. This internship not only provided me with those experiences, but also allowed me to build relationships with inspiring engineers; a takeaway I will never forget.

Composite Materials↗
DOE OSTI2018

Identification of a functional point defect in SrTi O 3

Unveiling both the presence and nature of point defects is one of the biggest challenges in condensed matter physics and materials science. Particularly in complex oxides, even a minute amount of unavoidable point defects could generate novel physical phenomena and functions, such as visible light emission and ferroelectricity, yet it remains elusive to clearly identify such point defects. Here, taking SrTiO3 as a model system, we show that iterative feedback among atomic-layer- and stoichiometry-controlled thin-film epitaxy, hybrid density functional theory, and high-resolution cathodoluminescence spectroscopy allows for the identification of a functional cationic defect, the Ti antisite (TiSr) defect. Our cathodoluminescence measurements reveal sub-band-gap luminescence, whose spectral fine structures show excellent quantitative agreement, as well as a one-to-one correspondence, with the theoretically predicted optical transitions from intrinsic point defects such as TiSr. Guided by the theory and spectroscopic results, we also control a cation stoichiometry, and it in turn results in good systematics in cathodoluminescence spectra. Not limited to the identification of TiSr, this approach allows for more reliable, self-consistent defect study, and provides critical insight into a microscopic picture of point defects in complex oxides.

Lee, Daesu↗
DOE OSTI2014

On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets

Advanced materials characterization techniques with ever-growing data acquisition speed and storage capabilities represent a challenge in modern materials science, and new procedures to quickly assess and analyze the data are needed. Machine learning approaches are effective in reducing the complexity of data and rapidly homing in on the underlying trend in multi-dimensional data. Here, we show that by employing an algorithm called the mean shift theory to a large amount of diffraction data in high-throughput experimentation, one can streamline the process of delineating the structural evolution across compositional variations mapped on combinatorial libraries with minimal computational cost. Data collected at a synchrotron beamline are analyzed on the fly, and by integrating experimental data with the inorganic crystal structure database (ICSD), we can substantially enhance the accuracy in classifying the structural phases across ternary phase spaces. We have used this approach to identify a novel magnetic phase with enhanced magnetic anisotropy which is a candidate for rare-earth free permanent magnet.

Kusne, AG↗
NASA NTRS1988

Containerless science for materials processing

Containerless materials processing in space to restrict specimen deformation from hydrostatic pressure of contact with the container is discussed. Several containerless science studies are reviewed, including the study of drop dynamics, nucleation studies, metastable solid phases experiments, glass research, biological materials research, and protein crystal growth. The development of technologies for materials research in space are examined, including acoustic containerless systems, and electrostatic and electromagnetic containerless processing.

Wang, T. G.↗
DOE OSTI2024

Reactive Carbon Capture: Cooperative and Bifunctional Adsorbent-Catalyst Materials and Process Integration for a New Carbon Economy

To say the least, releasing CO 2 into the atmosphere is reaping undue environmental consequences given the ever-present increase in severe global weather events over the past five years. However, it can be argued that–at least in the confines of current technological capabilities–the atmospheric release of CO 2 is somewhat unavoidable given that even shifting toward clean energy sources–such as solar, nuclear, wind, battery, or H 2 power–incurs an initial carbon requirement by way of manufacturing the very production abilities through which “clean” energy is generated. Even years from now, experts agree that energy production will be diversified and–as the global population continues to drive the growth of global energy consumption–thermal power derived from carbon combustion is likely to remain one intrinsic energetic source, of which CO 2 will always be a byproduct. In this context, it is the responsibility of the scientific community to devise improved pathways of carbon management such that (i) the consequences of combustion on the global environment are reduced and (ii) carbon fuels can be leveraged in a sustainable fashion. In this Account, we discuss a pivotal perspective shift on CO 2 emissions derived from a considerable breakthrough in material science from our work on shape engineering of nanoporous adsorbents and catalysts. This account details the development of materials which no longer vilify CO 2 emissions as a valueless combustion byproduct, instead providing a path for them to become a potential feedstock. In more specific terms, this work details the development of structured, cooperative “bifunctional” materials (BFMs) comprised of (i) a high-temperature adsorbent and (ii) a heterogeneous catalyst that enable single-bed CO 2 capture and utilization in oxidative ethane dehydrogenation (ODHE), oxidative propane dehydrogenation (ODHP), and dry methane reforming (DMR) processes. This Account begins with the conceptual development of the BFMs in the powdered state, followed by detailing the first-ever reports of structuring the materials into facile honeycomb contactors by 3D printing. The Account then summarizes the impressive performance of the 3D-printed BFMs, specifically focusing on how their catalysts (metal oxides and perovskites) influence their reactive CO 2 capture performances in ODHE, ODHP, and DMR processes. Such promise of CO 2 -as-fuel offers a glimpse into the future of a diversified energy economy, in which CO 2 /fuel looping can play an important role. A major factor in achieving this future is, of course, developing an appropriately active catalyst; an account of whose first breakthroughs in material science are detailed herein.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Predicting material microstructure evolution via data-driven machine learning

The recent rapid rise in data-driven practices in the materials science domain has led to the development of efficient, generalizable, and accurate approaches for several applications, including: material property prediction, mining (micro)structure- property and (micro)structure-processing relationships, and characterization of material microstructures. Central to the materials science domain is linking microstructure to properties and performance, and critical to building such linkages is understanding how microstructures evolve as a function of environmental exposure or processing conditions (e.g., time, temperature, applied stress or strain, irradiation). This preview article summarizes recent work on the development of recurrent neural networks for predicting spatio-temporal microstructure evolution detailed by Yang et al in their manuscript entitled 'Self-supervised Learning and Prediction of Microstructure Evolution with Recurrent Neural Networks'.

machine learning (ML), materials characterization↗
DOE OSTI2015

Materials engineering tetrahedron economic evaluation tool

A new open-source project evaluation tool, entitle the Materials Engineering Tetrahedron (MET), has been developed to determine the economic viability of materials design, selection, processing, and validation costs associated with any infrastructure-based project. MET improves project design by providing an economic perspective to the traditional materials science tetrahedron, relating microstructure, processing, property, and performance through the introduction of value-based economic costs for each side of the tetrahedron. The resulting size and distortion, from a regular tetrahedron illustrates the balance between the system, component, or material fabrication project detailed. Furthermore, the MET model also allows for increased budget efficiency and the potential for improved identification of cost saving mechanisms.

36 MATERIALS SCIENCE↗
NASA NTRS2012

Overview of NASA's Microgravity Materials Research Program

The NASA microgravity materials program is dedicated to conducting microgravity experiments and related modeling efforts that will help us understand the processes associated with the formation of materials. This knowledge will help improve ground based industrial production of such materials. The currently funded investigations include research on the distribution of dopants and formation of defects in semiconductors, transitions between columnar and dendritic grain morphology, coarsening of phase boundaries, competition between thermally and kinetically favored phases, and the formation of glassy vs. crystalline material. NASA microgravity materials science investigators are selected for funding either through a proposal in response to a NASA Research Announcement or by participation in a team proposing to a foreign agency research announcement. In the latter case, a US investigator participating in a successful proposal to a foreign agency can then apply to NASA for funding of an unsolicited proposal. The program relies on cooperation with other aerospace partners from around the world. The ISS facilities used for these investigations are provided primarily by partnering with foreign agencies and in most cases the US investigators are working as a part of a larger team studying a specific area of materials science. The following facilities are to be utilized for the initial investigations. The ESA provided Low Gradient Facility and the Solidification and Quench Inserts to the Materials Research Rack/Materials Science Laboratory are to be used primarily for creating bulk samples that are directionally solidified or quenched from a high temperature melt. The CNES provided DECLIC facility is used to observe morphological development in transparent materials. The ESA provided Electro-Magnetic Levitator (EML) is designed to levitate, melt and then cool samples in order to study nucleation behavior. The facility provides conditions in which nucleation of the solid is not triggered from the wall and in which fluid flows in the sample can be controlled and manipulated. These conditions allow scientists ideal conditions for understanding the relative amounts and distribution of different phases that form in the solid. Finally, the Coarsening of Solid Liquid Melts hardware allows quenching of low temperature samples in the Microgravity Science Glovebox.

Downey, James Patton↗
DOE OSTI2026

Knowledge gaps for neuromorphic ionic computing

BACKGROUND Neuromorphic computing, inspired by the human brain’s ability to process information efficiently, represents a transformative approach to computation. In this Review, we explore the emerging field of neuromorphic ionic computing, which leverages ionic conduction and coupling to mimic neural processes, and identify critical knowledge gaps that must be addressed to realize its full potential. A central theme of the discussion is energy efficiency, a challenge that is both a limitation and an opportunity for this technology. Although complementary metal-oxide semiconductor (CMOS)–based neuromorphic technologies have made strides in scaling to billions of neurons and are increasingly applied in artificial intelligence and numerical computing, they remain orders of magnitude behind the human brain in terms of connectivity and energy efficiency. Neuromorphic ionic computing promises to overcome these limitations by leveraging the distinct architectural and operational principles of the brain. Our brains achieve this energy efficiency by combining several key features: using the same network elements to store and process information; using an incredibly complex and massively interconnected three-dimensional (3D) network of locally active elements that enables sparsity, robustness in the presence of noise, adaptation, and life-long learning; computing at comparatively low voltage and frequency; and last, taking advantage of a plethora of ions and small molecules as information carriers. Here, we propose that ionic computing systems can take advantage of similar features to achieve substantial gains in energy efficiency. ADVANCES Since the first reports of neuromorphic ionic behavior in nanofluidic channels, we have witnessed an explosion of reports that used ionic devices to produce synaptomimetic behaviors. However, achieving the goals of ionic computing requires not only implementation of much more sophisticated device functionality but also overcoming fundamental barriers in materials science, device architecture, and system integration. Current ionic devices, even those incorporating state-of-the-art materials, still suffer from limited functionality and stability, which restrict their performance and increase energy demands. Developing new materials with enhanced ionic properties is essential to overcome these limitations. Similarly, the design of neuromorphic devices must evolve to leverage the particular advantages of ionic processes. Existing architectures often follow a single-information-carrier logic of conventional electronics or are constructed of mesoscale fluidics, failing to capitalize on the energy-efficient mechanisms inherent to ionic systems or implement the multiple-information-carrier paradigm. Current neuromorphic chips focus on large-scale networks of analog memory elements based on mechanisms such as charge trap (flash), filamentary, phase change, or spin, which are built on top of a network of artificial CMOS neurons. Although such prototype networks have achieved impressive performance, it is difficult to envision how they can implement the key features such as massive connectivity, sophisticated plasticity, adaptability, sparsity, and “multichromatic” computing. Although small-scale devices have demonstrated promising results, integrating them, maintaining energy efficiency, and implementing temperature control as systems grow in complexity and size to computationally relevant scale remain major hurdles. Furthermore, interfacing neuromorphic ionic devices with existing computing technologies presents technical and conceptual challenges that will require innovative approaches that combine insights from neuroscience, materials science, and engineering. OUTLOOK Despite these challenges, the potential impact of neuromorphic ionic computing is profound with potential applications ranging from artificial intelligence to robotics and beyond. We also argue that neuromorphic ionic computing systems should not, at least in the beginning, compete with CMOS technologies but rather should focus on applications that require extreme energy efficiency with chemical and/or biological compatibility, such as biomedical applications (for example, brain-computer interfaces), environmental monitoring, and agricultural and food applications. Ultimately, this Review highlights the crucial role of interdisciplinary collaboration in advancing the field. Neuromorphic ionic computing is not merely a technological innovation; it represents a substantial step toward sustainable computation, aligning with the growing demand for energy-conscious solutions in a world that is increasingly reliant on data and computation.

Neuromorphic↗
DOE OSTI2022

Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

The ever-increasing demand for novel materials with superior properties inspires retrofitting traditional research paradigms in the era of artificial intelligence and automation. An autonomous experimental platform (AEP) has emerged as an exciting research frontier that achieves full autonomy via integrating data-driven algorithms such as machine learning (ML) with experimental automation in the material development loop from synthesis, characterization, and analysis, to decision making. In this review, we started with a primer to describe how to develop data-driven algorithms for solving material problems. Then, we systematically summarized recent progress on automated material synthesis, ML-enabled data analysis, and decision-making. Finally, we discussed the challenges and opportunities in an endeavor to develop the next-generation AEP for ultimately realizing an autonomous or self-driving laboratory. In conclusion, this review will provide insights for researchers aiming to learn the frontier of ML in materials science and deploy AEP in their labs for accelerating material development.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Materials Characterization: A Primer for Solid Phase Processing Applications

The Pacific Northwest National Laboratory (PNNL) undertook the Materials Characterization, Prediction, and Control (MCPC) Laboratory Directed Research and Development (LDRD) Project to advance understanding of nuclear material processing and enable multifold acceleration in the development and qualification of new material systems produced via advanced manufacturing methods, such as solid phase processing, for use in national security and advanced energy applications (Smith 2021). As a two-year LDRD investment requiring focused research, the MCPC project applied only a subset of the wide range of available destructive and nondestructive characterization methods to provide data to the predictive modeling and data analytics tasks. The purpose of this report is to review a wide range of destructive and nondestructive characterization methods that are relevant in solid-phase processing (SPP) applications, but not necessarily applied in the MCPC Project as a guide to the planning of characterization activities in future research. Particular attention is given to measured characteristics that can correlate to other material characteristics, with a particular interest in nondestructive evaluation (NDE) that can be applied to samples obtained in the MCPC Project. Destructive examinations include tensile tests, optical and electron microscopy, micro-hardness, and residual stress tests. NDE tests include surface visual inspection, eddy current examination for cracks, 4-point potential drop, ultrasound, x-ray, and computed tomography.

36 MATERIALS SCIENCE↗
DOE OSTI2025

MS25: Materials Science-Focused Benchmark Data Set for Machine Learning Interatomic Potentials

Here, we present MS25, a benchmark data set for evaluating machine learning interatomic potentials (MLIPs) across diverse materials-relevant systems including MgO surfaces, liquid water, zeolites, a catalytic Pt surface reaction, high-entropy alloys (HEAs), and disordered Zr-oxides. Five MLIP architectures (MACE, NequIP, Allegro, MTP, and Torch-ANI) are trained and tested, focusing not only on traditional metrics (energies, forces, and stresses) but also explicitly validating derived physical observables such as lattice constants, volumes, and reaction barriers. We find that most models reach comparable accuracy on standard error metrics across the simple systems, although equivariant MLIPs offer 1.5–2× improvements over nonequivariant MLIPs in energy and force error for structurally complex or compositionally disordered environments such as HEAs and Zr–O systems. Our analysis highlights that low errors in energy and force predictions do not guarantee reliable observables, emphasizing the necessity of explicit validation. We demonstrate limitations in cross-framework transferability, as models trained on one zeolite framework (CHA) fail to reliably generalize to predictions of structurally distinct frameworks (e.g., MFI). Size-extensive tests show some dependence on system size for MgO, resulting from forced periodicity. The HEA and Zr–O data sets are identified as challenging tests for future benchmarks and MLIP model architecture developments as they show significant differentiation in error between MLIP architectures and are still relatively difficult at 1000 training images. Moving forward, we recommend that benchmarking efforts shift their focus from marginal accuracy improvements in energy and force errors toward identifying and understanding model failure modes, rigorously assessing transferability, and evaluating how their errors affect observable predictions. For researchers looking to choose an MLIP architecture, we suggest selecting equivariant MLIP architectures if the complexity of the system is a challenge. For simple materials problems, auxiliary features such as integration with molecular dynamics engines, trade-offs between computational data set generation cost vs MLIP inference speed, and framework integration may play a more important decision factor than small differences in error metrics that are unlikely to matter for production-level research.

chemical structure↗