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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Smoky Mountain Data Challenge 2020: An Open Call to Solve Data Problems in the Areas of Neutron Science, Material Science, Urban Modeling and Dynamics, Geophysics, and Biomedical Informatics

The 2020 Smoky Mountains Computational Sciences and Engineering Conference enlists research scientists from across Oak Ridge National Laboratory (ORNL) to be data sponsors and help create data analytics challenges for eminent data sets at the laboratory. This work describes the significance of each of the seven data sets and their as- sociated challenge questions. The challenge questions for each data set were required to cover multiple difficulty levels. An international call for participation was sent to students, and researchers asking them to form teams of up to four people to apply novel data analytics techniques to these data sets.

Parete-Koon, Suzanne↗
DOE OSTI2022

Artificial intelligence in computational materials science

In this themed collection we aim to broadly review some of the critical, recent progress in the application of AI/ML to various aspects of computational materials science and materials science more broadly. In this collection spread across two issues, we have assembled a collection of articles from leaders in the broad domain of applying AI/ML, which we collectively refer to as ML, in computational materials science. Together these articles curate the critical, recent progress in the application of ML to various aspects of materials science. Furthermore, these include ML approaches for understanding and driving electron microscopy, designing energy materials and the discovery of principles and materials relevant to the design of materials for the future, studying crystal nucleation and growth, the use of ML to describe force fields governing material and molecular behavior, and other topics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine learning in materials science: From explainable predictions to autonomous design

The advent of big data and algorithmic developments in the field of machine learning (and artificial intelligence, in general) have greatly impacted the entire spectrum of physical sciences, including materials science. Materials data, measured or computed, combined with various techniques of machine learning have been employed to address a myriad of challenging problems, such as, development of efficient and predictive surrogate models for a range of materials properties, screening and down-selection of novel candidate materials for targeted applications, new methodologies to improve and further expedite molecular and atomistic simulations, with likely many more important developments to come in the foreseeable future. While the applications thus far have provided a glimpse of the true potential data-enabled routes have to offer, it has also become clear that further progress in this direction hinges on our ability to understand, explain and rationalize findings of a machine learning model in light of the domain-knowledge. This focused review provides an overview of the main areas where machine learning has been widely and successfully used in materials science. Subsequently, a brief discussion of several techniques that have been helpful in extracting physically-meaningful insights, causal relationships and design-centric knowledge from materials data is provided. Finally, we identify some of the imminent opportunities and challenges that materials community faces in this exciting and rapidly growing field.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Sandia's Research in Support of COVID-19 Pandemic Response: Materials Science

Sandia Materials Science Investment Area contributed to the SARS-CoV-2 virus and COVID-19 disease which represent the most significant pandemic threat in over 100 years. We completed a series of 7, short duration projects to provide innovative materials science research and development in analytical techniques to aid the neutralization of COVID-19 on multiple surfaces, approaches to rapidly decontaminate personal protective equipment, and pareto assessment of construction materials for manufacturing personal protective equipment. The developed capabilities and processes through this research can help US medical personnel, government installations and assets, first responders, state and local governments, and multiple federal agencies address the COVID-19 Pandemic.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accelerated data-driven materials science with the Materials Project

The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world. This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our efforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community.

Horton, Matthew K↗
DOE OSTI2023

Evolution of artificial intelligence for application in contemporary materials science

Abstract Contemporary materials science has seen an increasing application of various artificial intelligence techniques in an attempt to accelerate the materials discovery process using forward modeling for predictive analysis and inverse modeling for optimization and design. Over the last decade or so, the increasing availability of computational power and large materials datasets has led to a continuous evolution in the complexity of the techniques used to advance the frontier. In this Review, we provide a high-level overview of the evolution of artificial intelligence in contemporary materials science for the task of materials property prediction in forward modeling. Each stage of evolution is accompanied by an outline of some of the commonly used methodologies and applications. We conclude the work by providing potential future ideas for further development of artificial intelligence in materials science to facilitate the discovery, design, and deployment workflow. Graphical abstract

Materials Science↗
DOE OSTI2024

Molecules to Masterpieces: Bridging Materials Science and the Arts

Art and materials innovation have always been intertwined, dating back to the earliest human creations. In modern times, however, the increasing specialization of materials science often restricts artists' access to cutting-edge materials. Here, the materials science aspects of an art-science collaboration between artist Kimsooja and the Wiesner Lab at Cornell University, are detailed. The project involves the development of a custom-made iridescent block copolymer coating by means of self-assembly, originally applied to transparent window panels of a façade for the ≈14 m tall art installation: A Needle Woman: Galaxy Is a Memory, Earth is a Souvenir by artist Kimsooja. After several exhibitions in the US and Europe, the installation is now part of the permanent museum collection at Yorkshire Sculpture Park in Wakefield, UK. Full characterization of the solution blade-cast coatings show shear aligned, standing up lamellar morphologies that behave as volume-phase gratings with periodicities between 300 and 400 nm. Coatings are also applied to foldable (origami) paper and converted into iridescent porous ceramic materials. Furthermore, it is hoped this work inspires and informs communities across materials science, the arts, and architecture.

Architectural nanomaterials↗
DOE OSTI2024

Addressing Critical Problems in Materials Science Through Multiscale and Multimode Characterization (Project 1); Characterization and Optimization of Novel Triple-Conducting Oxide Materials for Energy Applications (Project 2) (CRADA Final Report)

PROJECT 1: Address critical problems in materials science and simultaneously advance the state-of-the-art in multiscale and multimode characterization using the combined advanced analytical capabilities and expertise of Colorado School of Mines (CSM) and the National Renewable Energy Laboratory (NREL). The primary effort of the Phase I of this CRADA is to establish the International Center for Multiscale Characterization using shared resources at both NREL and CSM. Phase II will focus on capability development and marketing, choosing candidate materials science issues in the areas of structure imaging, chemical composition mapping, and correlating properties and performance of materials for impact in energy-related, environmental and critical materials areas. The CRADA will be modified to include specific topics of concern in materials science to industry member partners. Advanced analytical capabilities and expertise at CSM and NREL will be used to advance materials understanding and performance through characterization of multiscale phenomena including structural imaging, chemical composition mapping, and other techniques correlating properties and performance of materials. PROJECT 2: As part of the International Center for Materials Characterization, work under Modification #1 will be led by Colorado School of Mines (CSM), working in collaboration with NREL staff to mentor and advise CSM postdoctoral researchers on set up of diffusion annealing experiments. The purpose of the modification is to provide for NREL staff to mentor and advise CSM postdoctoral researchers on set up of diffusion annealing experiments, including mentoring and advising the CSM-NREL team on proper Secondary Ion Mass Spectrometry (SIMS) data analysis as needed. SIMS measurements of 10-20 samples will be performed at NREL during the project duration.

08 HYDROGEN↗
DOE OSTI2022

Explainable machine learning in materials science

Abstract Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explainable artificial intelligence (XAI), an emerging research field that addresses the explainability of complicated machine learning models like deep neural networks (DNNs). This article attempts to provide an entry point to XAI for materials scientists. Concepts are defined to clarify what explain means in the context of materials science. Example works are reviewed to show how XAI helps materials science research. Challenges and opportunities are also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Atomate2: modular workflows for materials science

High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2's improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Frontier nonequilibrium materials science enabled by ultrafast electron methods

Most innovation in materials science and engineering resides in our ability to understand and control the intimate relationship between the structure of materials and their properties. Conventionally, the only route to discovering innovative new material properties has been to explore the structural and compositional phase space that is accessible at (or near) thermodynamic equilibrium. The characterization, manipulation and, ultimately, control of material properties far from equilibrium offers almost completely untapped possibilities for uncovering novel states and phases of materials. Investigations of materials far from equilibrium require the development of new techniques, which are capable of following dynamic processes in materials with extreme spatiotemporal resolution. Thus, ultrafast electron-based methods have become a major new frontier in materials science due to the capability of following dynamics on time scales as short as femtoseconds with the high spatial resolution and sensitivity afforded by electrons. The articles in this issue provide an exciting cross section of the novel research directions that have been enabled at this frontier. Imaging on ultrafast time scales carries critical information on phase transitions, fundamental processes involved in light-harvesting, magnetic, and plasmonic dynamics, the coupling of electronic and nuclear degrees of freedom in materials and has revealed previously hidden, nonequilibrium metastable states of matter that have no equilibrium analog.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science [Slides]

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Advanced Interactive 3D Visualization Tool for Customizable Analyses of Tomography Datasets in Material Science

Current methods for visualizing and analyzing 3D tomography datasets in materials science often lack the interactivity and depth required for detailed structural insights. This limitation restricts a researchers' ability to accurately interpret complex data, which is critical for advancing material innovations and understanding structural properties. To address this issue, we have developed a novel, web-based interactive 3D visualization and analysis tool from the Trame framework that offers customizable features to enhance data interpretability. The tool allows users to adjust parameters such as visible range, slice planes, data rotation, and layering, providing a more detailed and dynamic view of complex structures. Its user-friendly web interface increases the accessibility and ease of use for both novice and experienced researchers, to visualize large volumetric datasets. The tool supports a diverse range of data formats, making it versatile for various research applications. Unique capabilities include real-time data manipulation, automated feature detection, context-sensitive feedback, and real-time volume calculations and distributions per sliced region or layer, alongside the ability to quickly generate high-quality screenshots and videos for presentations and reports. These advancements offer a comprehensive solution for enhanced 3D data exploration, significantly improving the analysis process and communication of results in materials science.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Quantum-centric supercomputing for materials science: A perspective on challenges and future directions

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. Here in this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

2024 roadmap on magnetic microscopy techniques and their applications in materials science

Considering the growing interest in magnetic materials for unconventional computing, data storage, and sensor applications, there is active research not only on material synthesis but also characterisation of their properties. In addition to structural and integral magnetic characterisations, imaging of magnetisation patterns, current distributions and magnetic fields at nano- and microscale is of major importance to understand the material responses and qualify them for specific applications. In this roadmap, we aim to cover a broad portfolio of techniques to perform nano- and microscale magnetic imaging using superconducting quantum interference devices, spin centre and Hall effect magnetometries, scanning probe microscopies, x-ray- and electron-based methods as well as magnetooptics and nanoscale magnetic resonance imaging. The roadmap is aimed as a single access point of information for experts in the field as well as the young generation of students outlining prospects of the development of magnetic imaging technologies for the upcoming decade with a focus on physics, materials science, and chemistry of planar, three-dimensional and geometrically curved objects of different material classes including two-dimensional materials, complex oxides, semi-metals, multiferroics, skyrmions, antiferromagnets, frustrated magnets, magnetic molecules/nanoparticles, ionic conductors, superconductors, spintronic and spinorbitronic materials.

2D materials↗