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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Adaptive Discovery and Mixed-Variable Optimization of Next Generation Synthesizable Microelectronic Materials

Design of new microelectronic materials is characterized by several challenges such as high-dimensionality of the atomic structure-composition variable space, formidable cost of directly using high-fidelity simulations for design optimization, dispersity in literature-reported similar materials and synthesis methods, complex physical mechanisms, and mixed qualitative and quantitative design variables that lead to a disjointed design space. Even though machine learning (ML) techniques have been employed to expedite materials innovation, existing methods treat ML and design optimization as two separate processes, failing to resolve the fundamental challenges associated with high dimensionality and mixed-variable complexity. We have developed a ML enhanced mixed-variable material design optimization framework to efficiently extract useful information from existing data in literature and physics-based simulations to guide the autonomous search for optimal materials. Our proposed framework is composed of four computational modules: (1) a natural language processing (NLP) based virtual screening module, (2) classification based concept exploration module, (3) a density functional theory (DFT)-based high-fidelity evaluation model, and (4) a novel latent-variable Gaussian process (LVGP) ML model for mixed-variable problems with uncertainty quantification, which seamlessly integrates with Bayesian Optimization (BO) and achieves superb efficiency through embedded physics-based dimension reduction. Our approach is demonstrated and validated using the testbed of functional materials exhibiting metal-insulation transitions (MITs), with the targeted reversible resistivity changes (∼10^5) near room temperature. At the end of the 30-month project, we have developed a series of new ML techniques using NLP, conditional variational autoencoders, active learning, latent-variable Gaussian processes, integrated with Bayesian optimization. Our project has resulted in new predicted MITs compounds and improved understanding of MITs microscopic mechanisms, which in turn will revolutionize microelectronics science to provide energy-saving solutions. Our research has improved both creativity and efficiency in transforming rare-event discoveries of new functional materials to persistent innovations. In addition to open-sourcing the online MIT database and the classification model, the LVGP open source code has been downloaded more than 15,000 times within two years. More than 40 MIT compounds have been identified and many have been pursued experimentally via collaborators. The research results are published in close to 20 collaborative papers in high-impact journals, such as Chem. Mater., Appl. Phys. Rev., Sci. Rep., among others of design space.

36 MATERIALS SCIENCE↗
NASA NTRS2022

Design of Materials with Alchemite

Machine learning models that establish the relationships between materials processing and properties can enable inverse design of materials through active learning. Alchemite is a commercial software that can perform inverse materials design on sparse data. Here we evaluate Alchemite’s performance on a dataset of shape memory alloys and a dataset of heat exchangers compared to baseline random forest models. Alchemite had higher accuracy when making predictions on sparse data and was more accurate or nearly as accurate as random forests on complete datasets while also quantifying uncertainty. The software was also used to suggest processing steps and design parameters to optimize properties and performance; however, physical validation of the suggested design parameters was beyond the scope of this work. Several useful design insights were gained about the impact of the design parameters on properties and performance including the importance of dopant choice and amount for shape memory alloys and the importance of height and weight on the thermal resistance of heat exchangers.

Machine learning↗
NASA NTRS2014

Computational Design of Materials: Planetary Entry to Electric Aircraft and Beyond

NASA's projects and missions push the bounds of what is possible. To support the agency's work, materials development must stay on the cutting edge in order to keep pace. Today, researchers at NASA Ames Research Center perform multiscale modeling to aid the development of new materials and provide insight into existing ones. Multiscale modeling enables researchers to determine micro- and macroscale properties by connecting computational methods ranging from the atomic level (density functional theory, molecular dynamics) to the macroscale (finite element method). The output of one level is passed on as input to the next level, creating a powerful predictive model.

Materials Design↗
DOE OSTI2024

Denoising diffusion probabilistic models for generative alloy design

Inverse material design is an extremely challenging optimization task made difficult by, in part, the highly nonlinear relationship linking performance with composition. Quantitative approaches have improved significantly owing to advances in high throughput experimentation and computational thermodynamics. However, existing physics-based tools are mostly forward models; input a chemistry and obtain a prediction. More recently the materials community has leveraged advances in the machine learning community to establish novel inverse design frameworks. Very recently denoising diffusion probabilistic models have been shown to be extremely powerful generators producing synthetic data of various modalities e.g. images, text, audio, tables, etc.. In this work a novel framework for alloy design and optimization is proposed leveraging these class of models. Five key generative tasks are demonstrated (1) unconditional generation (2) composition conditioned generation (3) property conditioned generation (4) multi-feedstock conditioned generation and (5) generative optimization. These methods were tested on three case studies: high entropy alloy design, superalloy binder jet additive manufacturing, and in-situ dual-feedstock wire-arc additive manufacturing. Results indicate that the established models are extremely flexible, expressive, and robust. The architecture’s flexibility and training procedure empower the model to learn complex intra-compositional and composition-property relationships. Furthermore, the probabilistic nature of these models makes them well suited for addressing solution non-uniqueness and tackling uncertainty quantification tasks. While the fidelity and quantity of the underlying training data is paramount, we envision that future alloy design frameworks will make extensive use of these kinds of machine learning models as “search” tools bolstering the utility of experimental and computational approaches.

36 MATERIALS SCIENCE↗
NASA NTRS2010

Designs and Materials for Better Coronagraph Occulting Masks

New designs, and materials appropriate for such designs, are under investigation in an effort to develop coronagraph occulting masks having broad-band spectral characteristics superior to those currently employed. These designs and materials are applicable to all coronagraphs, both ground-based and spaceborne. This effort also offers potential benefits for the development of other optical masks and filters that are required (1) for precisely tailored spatial transmission profiles, (2) to be characterized by optical-density neutrality and phase neutrality (that is, to be characterized by constant optical density and constant phase over broad wavelength ranges), and/or (3) not to exhibit optical- density-dependent phase shifts. The need for this effort arises for the following reasons: Coronagraph occulting masks are required to impose, on beams of light transmitted through them, extremely precise control of amplitude and phase according to carefully designed transmission profiles. In the original application that gave rise to this effort, the concern has been to develop broad-band occulting masks for NASA s Terrestrial Planet Finder coronagraph. Until now, experimental samples of these masks have been made from high-energy-beam-sensitive (HEBS) glass, which becomes locally dark where irradiated with a high-energy electron beam, the amount of darkening depending on the electron-beam energy and dose. Precise mask profiles have been written on HEBS glass blanks by use of electron beams, and the masks have performed satisfactorily in monochromatic light. However, the optical-density and phase profiles of the HEBS masks vary significantly with wavelength; consequently, the HEBS masks perform unsatisfactorily in broad-band light. The key properties of materials to be used in coronagraph occulting masks are their extinction coefficients, their indices of refraction, and the variations of these parameters with wavelength. The effort thus far has included theoretical predictions of performances of masks that would be made from alternative materials chosen because the wavelength dependences of their extinction coefficients and their indices of refraction are such that that the optical-density and phase profiles of masks made from these materials can be expected to vary much less with wavelength than do those of masks made from HEBS glass. The alternative materials considered thus far include some elemental metals such as Pt and Ni, metal alloys such as Inconel, metal nitrides such as TiN, and dielectrics such as SiO2. A mask as now envisioned would include thin metal and dielectric films having stepped or smoothly varying thicknesses (see figure). The thicknesses would be chosen, taking account of the indices of refraction and extinction coefficients, to obtain an acceptably close approximation of the desired spatial transmittance profile with a flat phase profile

Balasubramanian, Kunjithapatham↗
DOE OSTI2025

Spanning surface interaction length scales for the design of industrial antifouling materials

Fouling of surfaces introduces significant operational and economic challenges in marine and membrane systems. While empirical fouling assays and surface science methodologies have independently introduced fouling resistant methodologies and functionalities within each of these respective application spaces, they remain limited by their inability to extrapolate findings across length scales and inform materials design strategies. This perspective emphasizes the need to bridge the macroscale fouling assays conducted in the marine community and molecular-scale insights obtained from the membranes community. Here, by uniting these approaches, generalized design strategies can be developed across applications, leading to the realization of effective antifouling materials over a wide spectrum of operating conditions and foulant types. Standardized and high-throughput methodologies are proposed as tools to unify research efforts to drive the development of next-generation antifouling coatings and treatments.

fouling assays↗
DOE OSTI2022

Systems and methods for designing new materials for superlubricity

A method for designing new materials for superlubricity comprises developing, on a computational system, a computational supercell comprising x unit cells of a base material, each unit cell comprising y atoms of the base material. The computational system replaces randomly chosen z atoms of the base material with an impurity atom of an impurity material to form a candidate material. The computational system determines volumetric strain of the candidate material. In response to the volumetric strain exceeding a predetermined threshold, the computational system determines that the candidate material has superlubricity. The computational system displays the candidate material to a user if the candidate material has superlubricity.

Narayanan, Badri↗
DOE OSTI2021

Accelerated Discovery and Design of Ultralow Lattice Thermal Conductivity Materials Using Chemical Bonding Principles

Semiconductors with very low lattice thermal conductivities are highly desired for applications relevant to thermal energy conversion and management, such as thermoelectrics and thermal barrier coatings. Although the crystal structure and chemical bonding are known to play vital roles in shaping heat transfer behavior, material design approaches of lowering lattice thermal conductivity using chemical bonding principles are uncommon. In this work, an effective strategy of weakening interatomic interactions and therefore suppressing lattice thermal conductivity based on chemical bonding principles is presented and a high-efficiency approach of discovering low κ L materials by screening the local coordination environments of crystalline compounds is developed. The resulting first-principles calculations uncover 30 hitherto unexplored compounds with (ultra)low lattice thermal conductivities from 13 prototype crystal structures contained in the Inorganic Crystal Structure Database. Furthermore, an approach of rationally designing high-performance thermoelectrics is demonstrated by additionally incorporating cations with stereochemically active lone-pair electrons. Here these results not only provide atomic-level insights into the physical origin of the low lattice thermal conductivity in a large family of copper/silver-based compounds but also offer an efficient approach to discover and design materials with targeted thermal transport properties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Designing the Insulation System for Motors in Electrified Aircraft: Optimization, Partial Discharge Issues and Use of Advanced Materials

Designing the insulation system for motors to be used in electrical aircraft requires efforts for maximizing specific power, but, in parallel, particular attention to achieve high reliability. As a major harm for organic insulation systems is partial discharges, design must be able to infer their likelihood during any operation stage and handle their potential inception. This paper proposes a new approach to carry out optimized or conservative insulation system designs which can provide the specified life at the chosen failure probability as well as look at the option of possibly reducing the risk of partial discharges to zero, at any altitude. Examples of designing turn, phase to ground and phase-to-phase insulation systems are reported, with cases where the design can be optimized and other cases where the optimized design does not pass IEC testing standard. Therefore, the limits for design feasibility as a function of the required level of safety and reliability are discussed, showing that the presence of partial discharges cannot be always avoided even through conservative design criteria. Therefore, the use of advanced, corona-resistant materials must be considered, in order to reach a higher, sometimes redundant, level of reliability.

Ramin, Robin (ORCID:0000000211455006)↗
DOE OSTI2026

Computational design of materials for nuclear reactors

Computational design for fission reactor materials is ready to accelerate the development and qualification of nuclear materials. This review is primarily aimed at computational materials scientists that seek to apply ICME to the development of fission reactor materials. We summarize reactor materials and technology, discuss reactor material development and qualification today, show how ICME is being applied to the unique requirements of reactor materials, and provide a future vision.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Mono-material product design with bio-based, circular, and biodegradable polymers

The traditional multi-material product design of plastic products significantly complicates both existing and emerging recycling processes. Here, we discuss the concept of mono-material product design, which is based on circular and biodegradable polymers made of a single monomer, delivering tailorable properties via molecular or macromolecular engineering without changing its chemical makeup or composition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2000

Structural and Machine Design Using Piezoceramic Materials: A Guide for Structural Design Engineers

Using piezoceramic materials is one way the design engineer can create structures which have an ability to both sense and respond to their environment. Piezoceramic materials can be used to create structural sensors and structural actuators. Because piezoceramic materials have transduction as a material property, their sensing or actuation functions are a result of what happens to the material. This is different than discrete devices we might attach to the structure. For example, attaching an accelerometer to a structure will yield an electrical signal proportional to the acceleration at the attachment point on the structure. Using a electromagnetic shaker as an actuator will create an applied force at the attachment point. Active material elements in a structural design are not easily modeled as providing transduction at a point, but rather they change the physics of the structure in the areas where they are used. Hence, a designer must not think of adding discrete devices to a structure to obtain an effect, but rather must design a structural system which accounts for the physical principles of all the elements in the structure. The purpose of this manual is to provide practicing engineers the information necessary to incorporate piezoelectric materials in structural design and machine design. First, we will review the solid-state physics of piezoelectric materials. Then we will discuss the physical characteristics of the electrical-active material-structural system. We will present the elements of this system which must be considered as part of the design task for a structural engineer. We will cover simple modeling techniques and review the features and capabilities of commercial design tools that are available. We will then cover practical how-to elements of working with piezoceramic materials. We will review sources of piezoceramic materials and built-up devices, and their characteristics. Finally, we will provide two design examples using piezoceramic materials, first as discrete actuators for vibration isolation, and second as structurally-distributed sensor/actuators for active acoustic control.

Inman, Daniel J.↗
NASA NTRS1972

Materials and design for ablative heat shields

The goal of the ablation technology program is to bring ablation research and development to a high state of readiness for application to space shuttle vehicles. Some recent results and current work in the areas of materials, designs, and refurbishment for ablative TPS are reported. Future research and development needs are also presented.

Dow, M. B.↗