Search NASA⌕ Search

SEARCH · Search NASA

Results for “Computational Design Of Materials”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

First-Principles Understanding of Optical Excitations within Low-Dimensional Materials

The objective of this project is to utilize first-principles computational approaches to understand optical excitations within two-dimensional monolayers and heterostructures, with the ultimate goal of designing new materials by modifying the chemical and physical structure on the nanoscale. In 2D heterostructures, the nature and movement of the exciton is determined to a large extent by the electron-electron and electron-phonon strengths within individual monolayers, as well as the coupling between layers, all of which in turn depend on chemistry, solid-state screening properties, and quantum confinement. Controlling the nature and migration properties of excitons requires an understanding of the complex relationship between electrons, phonons, defects, disorder, and dynamics. The aim of this project is to determine, by analysis of highly accurate density functional theory (DFT) and many-body perturbation theory (MBPT) calculations, how these complexities can be decomposed into simple, tunable parameters. The specific goals of this project are to 1) understand electron conductivity, absorption and transparency of monolayer semiconductors and metals; 2) develop a theory of excitons near defects in low dimensions; and 3) understand the influence of inter-layer interactions in heterostructures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

CARPE DIEM: Coupled Algorithms for Robust Partitioning of Equations for the Dynamic Interactions of Evolving Materials

From aircraft design to non-proliferation, technical and policy decisions are becoming increasingly reliant on simulation of complex, real-world, multi-physics systems involving multiple interacting domains. As the power of computers has grown, deficiencies associated with traditional low-order-accurate mechanisms for inter-domain coupling have become increasingly apparent. The CARPE DIEM project addressed such deficiencies in the context of fluid-structure interaction (FSI) by developing new algorithms and simulation techniques that are based on a novel and rigorous mathematical approach and that are designed for efficiency on modern high-performance computing platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Towards Automated Design of Polymer Precursors for Rational Design of Porous Carbon-Materials [Slides]

Overview: Developed capability enables easy building of realistic polymer structures and a straightforward evaluation of polymer properties using different computational approaches. Topics of relevance to LANL mission: fundamental understanding of gas, vapor, ion, and liquid transport in polymer materials with applications in carbon capture, clean energy, hydrogen production (e.g., fuel cells and electrolyzes); Toxic element removal; Design of polymer binders in explosives. Workforce Development: Project provided a valuable opportunity to for a PI to manage a LANL research effort, expand collaborations (e.g., MPA division), and establish research topic that aligns with the LANL mission; The capability built was highlighted in the proposal "Versatile Polyolefins-Based Ionomer Platform for Next Generation Polymer Electrolyte Membrane and Hydrogen Production" lead by InnoSense LLC in collaboration with MPA-1 and T-1 as a response to DOE's call DE-FOA-0003003: Science Foundations for Energy Earthshots.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Anisotropic yield models for lattice unit cell structures exploiting orthotropic symmetry

We report numerical homogenization enables efficient computational analysis and design of multiscale structures made of micro-architected materials including lattice unit cells. However, predicting yield is nontrivial because it requires accurate and efficient predictions of the maximum stress inside the homogenized unit cells. To address this challenge, we develop a macroscale anisotropic yield function for micro-architected materials. The yield function depends on the three-dimensional macroscale stress state and the parameters describing a family of micro-architectures, such as the radii of the struts in a lattice unit cell. To ensure accuracy, we determine the maximum stress using three-dimensional continuum finite-element analysis. To ensure efficiency, we construct surrogate models from the aforementioned high-fidelity results for yield prediction. To reduce simulation costs and surrogate modeling complexity, we leverage orthotropic symmetry commonly found in lattice unit cells. In this paper, we provide a thorough presentation of group representations and the systematic procedure to exploit orthotropic domain symmetry in homogenization and surrogate modeling. We illustrate the use of linear homogenization results to predict yield in specific unit cells without further simulations. We furthermore show that surrogate modeling presents a viable option for anisotropic yield prediction in a continuously parametrized family of micro-architectures. More specifically, despite the dimensionality and degree of nonlinearity in the maximum micro von Mises stress within the unit cell, the surrogate models can predict it with less than 5% error at least 90% of the time. Moreover, the largest under-prediction error, which is more critical than the over-prediction error, is typically less than 10%.

42 ENGINEERING↗
DOE OSTI2024

Revolutionizing Materials Design: The Intersection of Quantum Mechanics and Data Modeling

The field of materials design is currently experiencing a notable evolution, driven by the convergence of sophisticated computational methodologies based on first principles and data-driven modeling approaches. I will review our recent endeavors employing AI/ML to expedite first-principles simulations and mitigate traditional methods' temporal and spatial limitations. Central to our efforts is developing and utilizing ML interatomic potentials (MLPs) across a diverse spectrum of materials. We show that MLPs serve as invaluable tools for navigating the complexities of the simulations, such as understanding the behavior of MgO at extreme environments of ~1 terapascal and temperatures >10,000 Kelvin. Moreover, we show that MLPs can provide precise details of the intricate dynamics governing the oxidation processes of binary alloy systems due to the competition between surface segregation and reconstruction tendencies. In summation, advancements in MLPs open the door to fresh possibilities in material modeling and, ultimately, discovery.

Saidi, Wissam↗
DOE OSTI2022

Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning

The pressing need for novel materials that can serve rising demands in solar cell and optoelectronic technologies makes the nexus of halide perovskites, high-throughput computations, and machine learning, very promising. Ever increasing amounts of data on the structure, fundamental properties, and device performance of halide perovskites provide opportunities for learning chemical rules and design principles that make these materials attractive, and applying them across wide chemical spaces. In this work, we show that impurity properties of halide perovskites computed using density functional theory (DFT) can be combined with machine learning (ML) to deliver predictive models and quick identification of optoelectronically active impurity atoms. Our computation lead to the largest reported dataset of the formation energies and charge transition levels of Pb-site impurities in methylammonium lead halide (MAPbX 3 ) perovskites. Descriptors are defined to uniquely represent any impurity atom in any MAPbX 3 compound and mapped to the computed impurity properties using regression techniques such as Gaussian process regression, neural networks, and random forests. We use the best optimized predictive models to make predictions for hundreds of impurities across 9 MAPbX 3 compounds and create lists of dominating impurities, that is, impurities that can shift the equilibrium Fermi level in the perovskite as determined by native point defects. Finally, this accelerated screening powered by computations and machine learning can guide the identification of problematic impurities that may cause undesired recombination of charge carriers, as well as impurities that can be deliberately introduced to tune the perovskite conductivity and resulting photovoltaic absorption.

36 MATERIALS SCIENCE↗
DOE OSTI2024

AI-Enhanced Co-Design for Next-Generation Microelectronics: Innovating Innovation (Workshop Report)

The Artificial Intelligence Enhanced Co-Design for Next Generation Microelectronics virtual workshop was held April 4-5, 2023, and attended by subject matter experts from universities, industry, and national laboratories. This was the third in a series of workshops to motivate the research community to identify and address major challenges facing microelectronics research and production. The 2023 workshop focused on a set of topics from materials to computing algorithms, and included discussions on relevant federal legislation and such as the Creating Helpful Incentives to Produce Semiconductors and Science Act (CHIPS Act) which was signed into law in the summer of 2022. Talks at the workshop included edge computing in radiation environments, new materials for neuromorphic computing, advanced packaging for microelectronics, and new AI techniques. We also received project updates from several of the Department of Energy (DOE) microelectronics co-design projects funded in the fall of 2021, and from three of the Energy Frontier Research Centers (EFRCs) that had been funded in the fall of 2022. The workshop also conducted a set of breakout discussions around the five principal research directions (PRDs) from the 2018 Department of Energy workshop report: 1) define innovative material, device, and architecture requirements driven by applications, algorithms, and software; 2) revolutionize memory and data storage; 3) re-imagine information flow unconstrained by interconnects; 4) redefine computing by leveraging unexploited physical phenomena; 5) reinvent the electricity grid through new materials, devices, and architectures. We tasked each breakout group to consider one primary PRD (and other PRDs as relevant topics arose during discussions) and to address questions such as whether the research community has embraced co-design as a methodology and whether new developments at any level of innovation from materials to programming models requires the research community to reevaluate the PRDs developed back in 2018.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Evaluation of a 5kW Solid Oxide Electrolysis Cell Stack

Solid oxide electrolysis cells (SOECs) are a developing technology for hydrogen production. They are promising due to their utilization of thermal energy to reduce their electricity consumption. The Department of Energy (DOE) has set goals to reduce the price per kilogram of hydrogen, and Idaho National Laboratory (INL) is conducting research to develop and demonstrate advanced methods and technologies to achieve making hydrogen a more affordable, efficient, and sustainable energy source. In this project, INL is collaborating with a vendor to evaluate and validate the design of a 5kW SOEC stack. The test aims to demonstrate a safe startup, operation, and shutdown on INL’s 5kW test stand, providing third-party validation in a different environment. A successful 50-hour test with stable hydrogen production will pave the way for a subsequent 1000-hour test. This project emphasized learning about electrolysis and the various support systems, referred to as balance of plant (BOP) equipment. These systems required modification while swapping to the vendor 5kW SOEC stack, most prominently the furnace door design and the controls system. It also included gaining skills in creating computer-aided design (CAD) drawings for the new door while using available materials and ensuring it met high-temperature requirements. Upon completion of modifications to the test stand, the SOEC will be test-fitted with respective wiring and piping. Instrumentation and piping will be checked for leaks and quality. This must be completed before the vendor’s engineers arrive to observe the test plan, startup, and a collection of 50 hours of data. The vendor’s engineers will ensure their stack performed sufficiently in the 50-hour test to enable the progression to a 1000-hour test.

08 - HYDROGEN↗
DOE OSTI2023

Preliminary Design of Critical Experiments Involving Commercially Available B 4 C Neutron Absorber Plates with Low-Enriched UO 2 Fuel

The International Criticality Safety Benchmark Evaluation Project (ICSBEP) is an initiative to provide high-quality benchmark data in a standardized format for criticality safety analysts to use to validate calculational tools and nuclear data. In the last released 2022 version, the ICSBEP handbook contains over 5,000 critical experiment descriptions, results, and associated models. This paper describes the critical configurations proposed and calculations performed as part of the conceptual design phase of Integral Experiment Request (IER) 554 (IER-554). The goal of IER-554 is to add to the benchmark experiments available to the criticality safety community by designing critical experiments that can analyze how adding commercially available Boralcan plates to an assembly of low-enriched UO2 fuel rods affects the effective neutron multiplication factor (k eff ). Boralcan, a neutron absorber product developed by Rio Tinto, is made of B4C in an Al 1100 matrix and is commonly used for criticality suppression in fuel storage pools. In the current version of the ICSBEP handbook, only a few dated critical experiments involve B4C materials, and none involve Boralcan in a thin plate shape. The experiments designed as part of IER-554 are intended to be performed at the Sandia Pulsed Reactor Facility/Critical Experiments (SPRF/CX) apparatus at Sandia National Laboratories. SPRF/CX is a well-characterized assembly considered trustworthy by the benchmarking community because of the numerous high-quality evaluations with very low experimental uncertainties included in the ICSBEP handbook. Figure 1 illustrates the assembly for a particular configuration from LCT-078, one of the published benchmarks in the ICSBEP handbook. The experiments will be moderated and reflected by light water at ambient atmospheric pressure. Before the plates are inserted in the critical assembly, they will be characterized with x-ray computed tomography (XCT) to identify the sizes and distribution of the B 4 C powder particles inside the plates. The preliminary design calculations were performed with SCALE 6.3.0 using the KENO V.a sequence for the criticality calculations, the CE-TSUNAMI-3D sequence for the sensitivity and uncertainty studies, and the ENDF/B-VII.1 Continuous Energy cross section library. All the calculation results presented have 10 pcm statistical uncertainties. A set of critical experiments with Boralcan plates could increase the confidence of the criticality safety community in its modeling methods when using this type of neutron absorber material. Experiments would also help validate k eff calculations, and the industry could use these validations to change the B loading credit limits from the US Nuclear Regulatory Commission standard review plan for dry cask storage of spent nuclear fuel. This paper summarizes only part of the analysis documented in the preliminary design report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Graph-based design of irregular metamaterials

In the field of metamaterial research, random structures offer a novel and less conventional approach compared to traditional periodic designs. Designing random metamaterials is challenging when it comes to ensuring intercon- nectivity, which is essential for manufacturability. This study introduces an innovative framework for generating random metamaterials using graph al- gorithms, ensuring connectivity and adaptability across various base shapes, including cylinders, triangles, pyramids, and cubes. By employing graph algorithms, our framework enhances the intuitiveness and efficiency of de- sign representation and manipulation, streamlining the design process. The framework generates families of designs that exhibit a wide range of prop- erty magnitudes that can be adjusted intuitively by modifying the input parameters. The rapid design process allows many designs to be generated, offering the user a multitude of solutions around the target property range. The designs can be effectively implemented in various fields and subjected to diverse analytical studies, including static, dynamic, and eigenfrequency assessments. We illustrate computational results for two key properties (stiff- ness and acoustic impedance), showcasing the method’s effectiveness through examples ranging from rod-based to cube-based designs. Here, the framework not only advances metamaterial research but also creates new opportunities for innovation in fields requiring customized material properties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Experimental and computational analysis of bending fatigue failure in chopped carbon fiber chip reinforced composites

With a better balance among good mechanical performance, high freedom of design, and low material and manufacturing cost, chopped carbon fiber chip reinforced sheet molding compound (SMC) composites show great potential in different engineering applications. Here in this paper, bending fatigue behaviors of SMC composites considering the heterogeneous fiber orientation distributions have been thoroughly investigated utilizing both experimental and computational methods. First, four-point bending fatigue tests are performed with designed SMC composites, and the local modulus is adopted as a metric to represent the local fiber orientation of two opposing sides. Interestingly, SMC composites with and without large discrepancy in local modulus of opposing sides show different fatigue behaviors. Interrupted tests are conducted to explore the bending fatigue failure mechanism, and the damage processes of valid specimens are also closely examined. We find that the fatigue failure of SMC composites under four-point bending is governed by crack propagation instead of crack initiation. Because of this, the heterogeneous local fiber orientations of both sides of the specimen influence fatigue life. The microstructure of the lower side shows a direct influence while that of the upper side also exhibiting influence which becomes more prominent for high cycle fatigue cases. Furthermore, a hybrid micro–macro computational model is proposed to efficiently study the cyclic bending behavior of SMC composites. The region of interest is reconstructed with a modified random sequential absorption algorithm to conserve all the microstructural details including the heterogeneous fiber orientation, while the rest of the regions are modeled as homogenized macro-scale continua. Combined with a framework to capture the progressive fatigue damage under cyclic bending, the bending fatigue behaviors of SMC composites are accurately captured by the hybrid computational model comparing with our experimental analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Defects go green: using defects in nanomaterials for renewable energy and environmental sustainability

Induction of point defects in nanomaterials can bestow upon them entirely new physics or augment their pre-existing physical properties, thereby expanding their potential use in green energy technology. Predicting structure-property relationships for defects a priori is challenging, and developing methods for precise control of defect type, density, or structural distribution during synthesis is an even more formidable task. Hence, tuning the defect structure to tailor nanomaterials for enhanced device performance remains an underutilized tool in materials design. We review here the state of nanomaterial design through the lens of computational prediction of defect properties for green energy technology, and synthesis methods to control defect formation for optimal performance. We illustrate the efficacy of defect-focused approaches for refining nanomaterial physics by describing several specific applications where these techniques hold potential. Most notably, we focus on quantum dots for reabsorption-free solar windows and net-zero emission buildings, oxide cathodes for high energy density lithium-ion batteries and electric vehicles, and transition metal dichalcogenides for electrocatalytic green hydrogen production and carbon-free fuels.

14 SOLAR ENERGY↗
DOE OSTI2023

A novel design of transitional layer structure between reduced activation ferritic martensitic steels and tungsten for plasma facing materials

Plasma-facing components (PFCs) are among the most critical gaps for fusion energy to establish technical and economic feasibility. Tungsten as a first wall/blanket material in PFCs requires to be integrating with reduced activation ferritic martensitic (RAFM) steels as a structural component. Currently, major drawbacks are the requirement of brazing, the formation of a brittle interface, and a large difference between the coefficients of thermal expansion of tungsten and steel. Here, a novel transitional multilayer structure was designed and investigated to join tungsten and RAFM steels using three interlayers. The composition of each interlayer was selected based on computational thermodynamics and diffusion kinetics to ensure a body-centered cubic (bcc) single-phase structure and prevent the formation of a brittle intermetallic phase region in the temperature range of 600–1150 °C. Although the transitional layer structure was designed for additive manufacturing, spark plasma sintering (SPS) as proof of concept was used to bond the individual layers. Interfaces were investigated using scanning and transmission electron microscopy methods but no layered intermetallic phase was observed. Nanoindentation maps across the interface suggest major hardness differences at the interface between tungsten and the vanadium interlayer, as well as the interface between RAFM steel and the FeCrAl interlayer.

36 MATERIALS SCIENCE↗
DOE OSTI2025

From Structured Solvents to Hybrid Materials (SS2HM) for Chemically Selective Capture and Electromagnetic Release of CO 2 : Mechanisms, Stability and Interfaces (Final Report)

The goal of this research program was to develop high capacity sorbents amenable for alternative regeneration approaches for direct air capture (DAC) of CO 2 . In particular, the research aimed to develop an understanding of CO 2 binding mechanism, thermal and oxidative stability, and regeneration energetics of functionalized ionic liquids (ILs), deep eutectic solvents (DESs), and porous materials. ILs and DESs are high-dielectric solvents with structural tunability that permits the rational-design for energy-efficient regeneration approaches based on electromagnetic (EM) field and moisture-swing. By further incorporating these solvents into polymeric capsules and other structural supports, multi-scale interfaces for targeted CO 2 and energy transfers were achieved. Aspects related to CO 2 capacity, selectivity, stability, dielectric properties, and binding energies were examined through experimental and computational design to identify molecular descriptors to inform future design of structured solvents and hybrid materials for DAC. Enclosed final report details the key findings, science advancements, and workforce development efforts from this project.

36 MATERIALS SCIENCE↗
DOE OSTI2021

hydro-hifem v.1.0

SAND2021-3180 O The hydro-hifem 1.0 software is used to simulate transient physical processes in large geologic environments consisting of complex fractures and man-made infrastructures with affordable computational cost and mesh refinement. The Earth models with such complex model features can be designed by considering the reduction of insignificant dimensions of fractures/infrastructures and embracing their corresponding hierarchical material properties, and their computational meshes are generated by using Cubit. The finite element solutions are automatically distributed across nodes of parallel cluster for efficient computation of transient fluid flow or heat conduction in geologic media. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Beskardes, GungorDidem↗
DOE OSTI2024

Recent Advances on Computational Modeling of Supported Single-Atom and Cluster Catalysts: Characterization, Catalyst–Support Interaction, and Active Site Heterogeneity

To satisfy the need for catalyst materials with high activity, selectivity, and stability for energy conversion, material design and discovery guided by theoretical insights are a necessity. In the past decades, the rise in theoretical investigations into the properties of catalyst materials, reaction mechanisms, and catalyst design principles has shed light on the catalysis field. Quantitative structure–activity relationships have been developed through incorporating spectroscopic simulations, electronic structure calculations, and reaction mechanistic studies. Here, in this review, we report the state-of-the-art computational approaches to catalyst materials characterization for supported single-atom and cluster catalysts utilizing spectroscopic simulations, i.e., XANES simulation, and material properties investigation via electronic-structure calculations. Furthermore, approaches regarding reaction mechanisms, focusing on active site heterogeneity, are also discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Topology optimization for the design of porous electrodes

Porous electrodes are an integral part of many electrochemical devices since they have high porosity to maximize electrochemical transport and high surface area to maximize activity. Traditional porous electrode materials are typically homogeneous, stochastic collections of small-scale particles and offer few opportunities to engineer higher performance. Fortunately, recent breakthroughs in advanced and additive manufacturing are yielding new methods to structure and pattern porous electrodes across length scales. These architected electrodes are emerging as a promising new technology to continue to drive improvement; however, it is still unclear which structures to employ and few tools are available to guide their design. Here we address this gap by applying topology optimization to the design of porous electrodes. We demonstrate our framework on two applications: a porous electrode driving a steady Faradaic reaction and a transiently operated electrode in a supercapacitor. We present computationally designed electrodes that minimize energy losses in a half-cell. For low-conductivity materials, the optimization algorithm creates electrode designs with a hierarchy of length scales. Further, the designed electrodes are found to outperform undesigned, homogeneous electrodes. Finally, we present three-dimensional porous electrode designs. We thus establish a topology optimization framework for designing porous electrodes.

25 ENERGY STORAGE↗
DOE OSTI2021

Multiscale design of nonlinear materials using a Eulerian shape optimization scheme

Motivated by recent advances in manufacturing, the design of materials is the focal point of interest in the material research community. One of the critical challenges in this field is finding optimal material microstructure for a desired macroscopic response. This work presents a computational method for the mesoscale-level design of particulate composites for an optimal macroscale-level response. The method relies on a custom shape optimization scheme to find the extrema of a nonlinear cost function subject to a set of constraints. Three key “modules” constitute the method: multiscale modeling, sensitivity analysis, and optimization. Multiscale modeling relies on a classical homogenization method and a nonlinear NURBS-based generalized finite element scheme to efficiently and accurately compute the structural response of particulate composites using a nonconformal discretization. A three-parameter isotropic damage law is used to model microstructure-level failure. An analytical sensitivity method is developed to compute the derivatives of the cost/constraint functions with respect to the design variables that control the microstructure's geometry. The derivation uncovers subtle but essential new terms contributing to the sensitivity of finite element shape functions and their spatial derivatives. Several structural problems are solved to demonstrate the applicability, performance, and accuracy of the method for the design of particulate composites with a desired macroscopic nonlinear stress-strain response.

42 ENGINEERING↗