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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

An open experimental database for exploring inorganic materials

The use of advanced machine learning algorithms in experimental materials science is limited by the lack of sufficiently large and diverse datasets amenable to data mining. If publicly open, such data resources would also enable materials research by scientists without access to expensive experimental equipment. Here, we report on our progress towards a publicly open High Throughput Experimental Materials (HTEM) Database (htem.nrel.gov). This database currently contains 140,000 sample entries, characterized by structural (100,000), synthetic (80,000), chemical (70,000), and optoelectronic (50,000) properties of inorganic thin film materials, grouped in >4,000 sample entries across >100 materials systems; more than a half of these data are publicly available. This article shows how the HTEM database may enable scientists to explore materials by browsing web-based user interface and an application programming interface. This paper also describes a HTE approach to generating materials data, and discusses the laboratory information management system (LIMS), that underpin HTEM database. Finally, this manuscript illustrates how advanced machine learning algorithms can be adopted to materials science problems using this open data resource.

36 MATERIALS SCIENCE↗
DOE OSTI2020

An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction

Micrograph quantification is an essential component of several materials science studies. Machine learning methods, in particular convolutional neural networks, have previously demonstrated performance in image recognition tasks across several disciplines (e.g. materials science, medical imaging, facial recognition). Here, we apply these well-established methods to develop an approach to microstructure quantification for kinetic modeling of a discontinuous precipitation reaction in a case study on the uranium-molybdenum system. Prediction of material processing history based on image data (classification), calculation of area fraction of phases present in the micrographs (segmentation), and kinetic modeling from segmentation results were performed. Results indicate that convolutional neural networks represent microstructure image data well, and segmentation using the k-means clustering algorithm yields results that agree well with manually annotated images. Classification accuracies of original and segmented images are both 94% for a 5-class classification problem. Kinetic modeling results agree well with previously reported data using manual thresholding. The image quantification and kinetic modeling approach developed and presented here aims to reduce researcher bias introduced into the characterization process, and allows for leveraging information in limited image data sets.

36 MATERIALS SCIENCE↗
NASA NTRS1989

The Space Station Freedom - International cooperation and innovation in space safety

The Space Station Freedom (SSF) being developed by the United States, European Space Agency (ESA), Japan, and Canada poses novel safety challenges in design, operations, logistics, and program management. A brief overview discloses many features that make SSF a radical departure from earlier low earth orbit (LEO) space stations relative to safety management: size and power levels; multiphase manned assembly; 30-year planned lifetime, with embedded 'hooks and scars' forevolution; crew size and skill-mix variability; sustained logistical dependence; use of man, robotics and telepresence for on-orbit maintenance of station and free-flyer systems; closed-environment recycling; use of automation and expert systems; long-term operation of collocated life-sciences and materials-science experiments, requiring control and segregation of hazardous and chemically incompatible materials; and materials aging in space.

Rodney, George A.↗
DOE OSTI2017

First-Principles Simulations of Functional Materials for Energy Conversion

Computational modeling has become a very effective approach in predicting properties of materials, and in designing functional materials with targeted structural, thermal, or optical properties. Many electronic structure methods have been developed, including density functional theory. Among them, DFT has been widely used in physics and materials science because it is computationally cheaper than other methods but still gives desired accuracy. It also provides a good starting point for higher levels of theory, such as many-body perturbation theory and quantum Monte Carlo. In parallel to these electronic structure method developments, a dramatic increase in computing capabilities over the last decade has enabled large-scale electronic structure calculations to address leading-edge materials science problems. In particular, with Theta at the Argonne Leadership Computing Facility (ALCF), our early science project investigated large-scale nanostructured materials for energy conversion and storage using two open-source electronic structure codes Qbox (http://qboxcode.org) and WEST (http://west-code.org). Qbox is an ab-initio molecular dynamics code based on plane wave DFT, and WEST is a post-DFT code for excited state calculations within many-body perturbation theory.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Self-driving thin film laboratory: autonomous epitaxial atomic-layer synthesis via real-time computer vision analysis of electron diffraction

Emerging materials science platforms with the ability to make autonomous decisions on the fly are fundamentally changing the outlook and protocols for materials optimization and discovery. Because AI-driven self-navigating schemes can effectively reduce the total number of iterations needed to arrive at the "answer" (i.e. the best stochiometric composition for a desired physical property, optimum materials processing parameters, etc.) by significant margins, they have the potential to revolutionize materials and chemical manufacturing processes at large in research laboratory settings as well as in industrial plants. Here, we demonstrate a successful implementation of real-time closed-loop autonomous navigation of a multi-dimensional materials synthesis parameter space for fabricating phase-pure epitaxial films of a metastable phase of a functional oxide in a combinatorial pulsed laser deposition chamber. Sequential epitaxial growth iterations in search of the optimized recipe to stabilize the desired crystal phase were performed using frame-by-frame quantitative computer vision analysis of reflection high-energy electron diffraction (RHEED) images of the unit-cell level film being deposited. The autonomous scheme regularly resulted in > 30-fold reduction in the number of required experiments compared to a comprehensive mapping of the parameter space. The real-time workflow developed here can be readily extended to a variety of thin film synthesis platforms opening the door for self-driving atomic-level materials design as well as autonomous optimization of semiconductor manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2018

A practical field guide to thermoelectrics: Fundamentals, synthesis, and characterization

The study of thermoelectric materials spans condensed matter physics, materials science and engineering, and solid-state chemistry. The diversity of the participants and the inherent complexity of the topic mean that it is difficult, if not impossible, for a researcher to be fluent in all aspects of the field. This review, which grew out of a one-week summer school for graduate students, aims to provide an introduction and practical guidance for selected conceptual, synthetic, and characterization approaches and to craft a common umbrella of language, theory, and experimental practice for those engaged in the field of thermoelectric materials. This review does not attempt to cover all major aspects of thermoelectric materials research or review state-of-the-art thermoelectric materials. Rather, the topics discussed herein reflect the expertise and experience of the authors. We begin by discussing a universal approach to modeling electronic transport using Landauer theory. The core sections of the review are focused on bulk inorganic materials and include a discussion of effective strategies for powder and single crystal synthesis, the use of national synchrotron sources to characterize crystalline materials, error analysis, and modeling of transport data using an effective mass model, and characterization of phonon behavior using inelastic neutron scattering and ultrasonic speed of sound measurements. The final core section discusses the challenges faced when synthesizing carbon-based samples and the measuring or interpretation of their transport properties. We conclude this review with a brief discussion of some of the grand challenges and opportunities that remain to be addressed in the study of thermoelectrics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Assessing Local Structure Motifs Using Order Parameters for Motif Recognition, Interstitial Identification, and Diffusion Path Characterization

Structure–property relationships form the basis of many design rules in materials science, including synthesizability and long-term stability of catalysts, control of electrical and optoelectronic behavior in semiconductors, as well as the capacity of and transport properties in cathode materials for rechargeable batteries. The immediate atomic environments (i.e., the first coordination shells) of a few atomic sites are often a key factor in achieving a desired property. Some of the most frequently encountered coordination patterns are tetrahedra, octahedra, body and face-centered cubic as well as hexagonal close packed-like environments. Here, we showcase the usefulness of local order parameters to identify these basic structural motifs in inorganic solid materials by developing classification criteria. We introduce a systematic testing framework, the Einstein crystal test rig, that probes the response of order parameters to distortions in perfect motifs to validate our approach. Subsequently, we highlight three important application cases. First, we map basic crystal structure information of a large materials database in an intuitive manner by screening the Materials Project (MP) database (61,422 compounds) for element-specific motif distributions. Second, we use the structure-motif recognition capabilities to automatically find interstitials in metals, semiconductor, and insulator materials. Our Interstitialcy Finding Tool (InFiT) facilitates high-throughput screenings of defect properties. Third, the order parameters are reliable and compact quantitative structure descriptors for characterizing diffusion hops of intercalants as our example of magnesium in MnO 2 -spinel indicates. Finally, the tools developed in our work are readily and freely available as software implementations in the pymatgen library, and we expect them to be further applied to machine-learning approaches for emerging applications in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Investigating Deformation and Failure Mechanisms in Nanoscale Multilayer Metallic Composites

Over the history of materials science there are many examples of materials discoveries that have made superlative materials; the strongest, lightest, or toughest material is almost always a goal when we invent new materials. However, often these have been a result of enormous trial and error approaches. A new methodology, one in which researchers design, from the atoms up, new ultra-strong materials for use in energy applications, is taking hold within the science and engineering community. This project focused on one particular new classification of materials; nanolaminate metallic composites. These materials, where two metallic materials are intimately bonded and layered over and over to form sheets or coatings, have been shown over the past decade to reach strengths over 10 times that of their constituents. However, they are not yet widely used in part because while extremely strong (they don’t permanently bend), they are also not particularly tough (they break relatively easily when notched). Our program took a coupled approach to investigating new materials systems within the laminate field. We used computational materials science to explore ways to institute new deformation mechanisms that occurred when a tri-layer, rather than the more common bi-layer system was created. Our predictions suggested that copper-nickel or copper-niobium composites (two very common bi-layer systems) with layer thicknesses on the order of 20 nm and then layered 100’s of times, would be less tough than a copper-nickel-niobium metallic composite of similar thicknesses. In particular, a particular mode of permanent deformation, cross-slip, could be activated only in the tri-layer system; the crystal structure of the other bi-layers would prohibit this particular mode of deformation. We then experimentally validated this predication using a wide range of tools. We utilized a DOE user facility, the Center for Integrated Nanotechnology (CINT), to fabricate, for the first time, these tri-layer composites. CINT formed nanolaminate composites were tested in tension, with bulge testing, using nanoindentation, and using micro-compression testing to demonstrate that the tri-layer films were indeed tougher and hardened more during deformation (they got stronger as we deformed them) than equivalent bi-layers. The seven graduate students, 4 post-docs and research faculty, and the two faculty co-PI’s were able to create a collaborated computational prediction and experimental validation team to demonstrate the benefits of this class of materials to the community. The computational work crossed from atomistic to bulk simulations, and the experiments coupled form nm-scale to the mm scale; closely matching the simulations. The simulations provided viable mechanisms that explained the observed results, and new experimental results were used to push the boundaries of the simulation tools. Over the life of the 7 years of this program we proved that tri-layer nanolaminate metallic composite systems exceeded the mechanical performance of bi-layer systems if the right materials were chosen, and that the mechanism responsible for this was tied to the cross slip of dislocations. With 30 journal publications resulting from this work we have broadly disseminated this family of results to the scientific community.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Phase Stability Through Machine Learning

Understanding the phase stability of a chemical system constitutes the foundation of materials science. Knowledge of the equilibrium state of a system under arbitrary thermodynamic conditions provides valuable information about the types of phases that are likely to be synthesized and how to get there. Accessing the phase diagram in a materials system provides one with the information necessary to design materials and microstructures with optimal properties. While the materials science community has long been focused on exploiting this knowledge to navigate the materials space, recent advances in machine learning (ML) and artificial intelligence (AI) have provided the community with novel ways of interrogating the materials thermodynamics space. Furthermore, this work presents some of the most recent advances in ML/AI applied to phase stability and thermodynamics of materials. Prof. John Morral always had a passion for understanding and teaching the fundamental characteristics of phase diagrams. This review is written to honor his memory.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Materials challenges for quantum technologies based on color centers in diamond

Emerging quantum technologies require precise control over quantum systems of increasing complexity. Defects in diamond, particularly the negatively charged nitrogen-vacancy center, are a promising platform with the potential to enable technologies ranging from ultra-sensitive nanoscale quantum sensors, to quantum repeaters for long distance quantum networks, to simulators of complex dynamical processes in many-body quantum systems, to scalable quantum computers. While these advances are due in large part to the distinct material properties of diamond, the uniqueness of this material also presents difficulties, and there is a growing need for novel materials science techniques for characterization, growth, defect control, and fabrication dedicated to realizing quantum applications with diamond. In this article we identify and discuss the major materials science challenges and opportunities associated with diamond quantum technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Advancing Development and Application of Superalloys

This JOM topic is sponsored by the High Temperature Alloys Committee of TMS and addresses advances in the development and application of superalloys. Each year, the High Temperature Alloys Committee organizes symposia at the TMS and Materials Science and Technology annual meetings. In 2020, two symposia were organized for the TMS annual meeting on February 23–27 in San Diego, CA: Materials Design Approaches & Experiences V, co-sponsored by the Integrated Computational Materials Engineering Committee and Advancing Current and State-of-the-Art Applications of Ni- and Co-base Superalloys, co-sponsored by the Corrosion and Environmental Effects Committee. Later this year, the 14th International Symposium on Superalloys will be held on September 13-17 in Seven Springs, PA. The conference, held every 4 years, convenes members of the superalloy community to discuss recent challenges and discoveries affecting future performance and use of superalloys. The TMS High Temperature Alloys Committee meets at both the TMS Annual Meeting & Exhibition and the annual Materials Science and Technology conference. The meetings are open to anyone interested in high-temperature alloys. This special topic on Advancing Development and Application of Superalloys includes a range of sub-topics including mechanical performance, corrosion resistance, processing and advanced manufacturing techniques, which were submitted by research groups from industry, national laboratories and universities.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Technical Report

MEST has developed Materials Navigator, a machine-learning software tool, to extract/combine data from disparate sources of materials information, which can be used to make suggestions for new directions of search and discovery of materials. The goal is to create a flexible software which can help materials science researchers quickly focus on a small group of promising materials and conduct fewer and less expensive experiments for maximum impact. Materials domains of interest for the Materials Navigator include solid state electrolytes for batteries, materials for fuel cells, photovoltaics, caloric cooling materials, and permanent magnets. Our solution is useful to materials researchers in academics and industry. To date, the MEST team has already developed a prototype of the Materials Navigator, which can be used to visualize a large number of materials compounds in their “descriptor space” where materials properties are encoded in machine learning based quantities. We have used Materials Navigator to make a list of potential new materials for batteries. It has also led to the experimental discovery of a new tantalum oxide superconductor.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep-freeze graph training for latent learning

Scientific and engineering advances are primarily driven by multi-tier conceptual constructs and conditional theoretical frameworks. The theories allow predictions of hypothetical system responses, given a set of approximate conditions (ranges of applicability) imposed on latent parameters that cannot be measured directly. Learning to estimate the latent variables (Latent Learning) helps to pinpoint the anticipated range-edge anomalies and improves the confidence in interpretation, interpolation and extrapolation of limited experimental data. Due to high dimensionality and extreme non-linearity of the materials science problems, very large datasets are typically required for conventional data-driven model development. The vital experimental data collection, particularly on microstructural phases, is very challenging, which makes it difficult to compile a high-quality database. Incorporation of the domain knowledge into the computational graph structure, initialization and optimization processes presents a viable mechanism for developing accurate models, with limited datasets. Furthermore, this study successfully utilized the approach to build the Deep Freeze Graph (DeepFreG) by mapping known causality relationships and by digitizing empirical domain knowledge for Latent Learning (LL), with specific applications in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Synthetic data-driven deep learning for label-free autonomous atomic force microscopy

Atomic force microscopy (AFM) is a widely used tool for nanoscale characterization across materials science, energy research, and biology. However, its adoption in high-throughput materials discovery and statistically driven studies remains limited by a strong dependence on expert operator input and by the scarcity of annotated experimental AFM datasets needed to enable data-driven automation. Here, we introduce SimuScan, a synthetic-data–driven framework that enables reliable AFM feature identification, segmentation, and targeted imaging without requiring large manually labeled experimental datasets. SimuScan generates tunable, high-fidelity synthetic AFM images of defined morphologies while incorporating realistic experimental artifacts, including tip–sample convolution, noise, flattening distortions, and surface debris. These datasets are shown to support scalable, label-free training of modern deep learning models for AFM analysis. When integrated into data-driven AFM workflows, SimuScan-trained models can locate and analyze nanoscale structures across large datasets and guide targeted follow-up imaging. We validate this approach on nanostructured surfaces, DNA assemblies, and bacterial cells, demonstrating robust generalization across diverse sample types with minimal operator intervention. More broadly, this work establishes a general strategy for generating explicitly conditioned, task-relevant synthetic data to improve the reliability of downstream models in autonomous microscopy.

Millan-Solsona, Ruben [Oak Ridge National Laborato↗
DOE OSTI2016

Column-by-column observation of dislocation motion in CdTe: Dynamic scanning transmission electron microscopy

The dynamics of partial dislocations in CdTe have been observed at the atomic scale using aberration-corrected scanning transmission electron microscopy (STEM), allowing the mobility of different dislocations to be directly compared: Cd-core Shockley partial dislocations are more mobile than Te-core partials, and dislocation cores with unpaired columns have higher mobility than those without unpaired columns. The dynamic imaging also provides insight into the process by which the dislocations glide. Dislocations with dangling bonds on unpaired columns are found to be more mobile because the dangling bonds mediate the bond exchanges required for the dislocations to move. Furthermore, a screw dislocation has been resolved to dissociate into a Shockley partial-dislocation pair along two different directions, revealing a way for the screw dislocation to glide in the material. The results show that dynamic STEM imaging has the potential to uncover the details of dislocation motion not easily accessible by other means. This research was sponsored by the U.S. DOE, Office of Energy Efficiency and Renewable Energy, Foundational Program to Advance Cell Efficiency (F-PACE, DE-FOA-0000492), (CL, YLW, NP, YFY, SJP), the Office of DOE-BES, Materials Science and Engineering Division (ARL), DOE Grant No. DE-FG02-09ER46554 (YYZ, STP), and a user project supported by ORNL's Center for Nanophase Materials Sciences (CNMS), which is also sponsored by DOE-BES. Supercomputer time was provided by the National Center for Supercomputing Applications which is supported by the DOE Office of Science under Contract No. DE-AC02-05CH11231, and the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation Grant No. ACI-1053575. Chen Li [No. 656378] and Timothy Pennycook [No.655760] are currently funded by the European Union's Marie Sklodowska-Curie Grants.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Cracking the code of multi-layer films to promote circularity in single-use plastic packaging

Multi-layer film packaging (MLF) revolutionized food preservation by combining diverse material layers to optimize barrier properties, mechanical strength, and shelf-life. These materials are essential for transporting perishables across various climates and allow for access to fresh goods in “food deserts”, but they pose significant recycling challenges due to their structural complexity. This perspective examines key structure-property relationships governing barrier performance and highlights innovations in material design. We explore how machine learning can predict performance metrics and propose recyclable alternatives, integrating data-driven approaches with material science insights. By challenging the status quo of MLF design, we advocate for circularity in food packaging, inspiring innovation at the intersection of sustainability, material science, and artificial intelligence.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Solving complex nanostructures with ptychographic atomic electron tomography

Abstract Transmission electron microscopy (TEM) is essential for determining atomic scale structures in structural biology and materials science. In structural biology, three-dimensional structures of proteins are routinely determined from thousands of identical particles using phase-contrast TEM. In materials science, three-dimensional atomic structures of complex nanomaterials have been determined using atomic electron tomography (AET). However, neither of these methods can determine the three-dimensional atomic structure of heterogeneous nanomaterials containing light elements. Here, we perform ptychographic electron tomography from 34.5 million diffraction patterns to reconstruct an atomic resolution tilt series of a double wall-carbon nanotube (DW-CNT) encapsulating a complex ZrTe sandwich structure. Class averaging the resulting tilt series images and subpixel localization of the atomic peaks reveals a Zr 11 Te 50 structure containing a previously unobserved ZrTe 2 phase in the core. The experimental realization of atomic resolution ptychographic electron tomography will allow for the structural determination of a wide range of beam-sensitive nanomaterials containing light elements.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Structure and Magnetic Properties of Lanthanide Nanocrystals

We have had considerable success on this project, particularly in the understanding of the relationship between nanostructure and magnetic properties in lanthanide nanocrystals. We also have successfully facilitated the doctoral degrees of Dr. Suseela Somarajan, in the Department of Physics and Astronomy, and Dr. Melissa Harrison, in the Materials Science Program. The following passages summarize the various accomplishments that were featured in 9 publications that were generated based on support from this grant. We thank the Department of Energy for their generous support of our research efforts in this area of materials science, magnetism, and electron microscopy.

36 MATERIALS SCIENCE↗