Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Materials Design”

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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Ultrathin Magnesium-Based Coating as an Efficient Oxygen Barrier for Superconducting Circuit Materials

Scaling up superconducting quantum circuits based on transmon qubits necessitates substantial enhancements in qubit coherence time. Over recent years, tantalum (Ta) has emerged as a promising candidate for transmon qubits, surpassing conventional counterparts in terms of coherence time. However, amorphous surface Ta oxide layer may introduce dielectric loss, ultimately placing a limit on the coherence time. In this study, a novel approach for suppressing the formation of tantalum oxide using an ultrathin magnesium (Mg) capping layer is presented. Synchrotron-based X-ray photoelectron spectroscopy studies demonstrate that oxide is confined to an extremely thin region directly beneath the Mg/Ta interface. Additionally, it is demonstrated that the superconducting properties of thin Ta films are improved following the Mg capping, exhibiting sharper and higher-temperature transitions to superconductive and magnetically ordered states. Moreover, an atomic-scale mechanistic understanding of the role of the capping layer in protecting Ta from oxidation is established based on computational modeling. Further, this work provides valuable insights into the formation mechanism and functionality of surface tantalum oxide, as well as a new materials design principle with the potential to reduce dielectric loss in superconducting quantum materials. Ultimately, the findings pave the way for the realization of large-scale, high-performance quantum computing systems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Towards physics-informed explainable machine learning and causal models for materials research

From emergent material descriptions to estimation of properties stemming from structures to optimization of process parameters for achieving best performance – all key facets of materials science and related fields have experienced tremendous growth with the introduction of data-driven models. This gradual progression goes at par with developments of machine learning workflows, from purely data-driven shallow models to those that are well-capable in encoding more complex graphs, symbolic representations, invariances, and positional embeddings. Furthermore, this perspective aims at summarizing strategic aspects of such transitions while providing insights into the requirements of bringing in explainable, interpretable predictive models, and causal learning to aid in materials design and discovery. Although the focus remains on a variety of functional materials by providing a handful of case studies, the applications of such integrated methodologies are universal to facilitate fundamental understandings of materials physics while enabling autonomous experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2020

H-Mat Hydrogen Compatibility of Polymers and Elastomers

The H2@Scale program of the U.S. Department of Energy (DOE) Fuel Cell Technologies Office is supporting work on the hydrogen compatibility of polymers to improve the durability and reliability of materials for hydrogen infrastructure. The hydrogen compatibility program (H-Mat) seeks “to address the challenges of hydrogen degradation by elucidating the mechanisms of hydrogen-materials interactions with the goal of providing science-based strategies to design materials, (micro)structures, and morphology with improved resistance to hydrogen degradation.” This research has observed interactions of hydrogen and pressure with model rubber-material compounds resulting in volume change and compression-set differences in the materials. The materials were investigated using helium-ion microscopy (HeIM), which revealed significant morphological changes in the plasticizer-incorporating compounds after exposure, as evidenced by time-of-flight secondary ion mass spectrometry. Additional studies using transmission electron microscopy and nuclear magnetic resonance were performed to correlate morphological change to potential chemical change in the materials.

08 HYDROGEN↗
DOE OSTI2023

Rapid design of top-performing metal-organic frameworks with qualitative representations of building blocks

Abstract Data-driven materials design often encounters challenges where systems possess qualitative (categorical) information. Specifically, representing Metal-organic frameworks (MOFs) through different building blocks poses a challenge for designers to incorporate qualitative information into design optimization, and leads to a combinatorial challenge, with large number of MOFs that could be explored. In this work, we integrated Latent Variable Gaussian Process (LVGP) and Multi-Objective Batch-Bayesian Optimization (MOBBO) to identify top-performing MOFs adaptively, autonomously, and efficiently. We showcased that our method (i) requires no specific physical descriptors and only uses building blocks that construct the MOFs for global optimization through qualitative representations, (ii) is application and property independent, and (iii) provides an interpretable model of building blocks with physical justification. By searching only ~1% of the design space, LVGP-MOBBO identified all MOFs on the Pareto front and 97% of the 50 top-performing designs for the CO 2 working capacity and CO 2 /N 2 selectivity properties.

36 MATERIALS SCIENCE↗
NASA NTRS1979

Flight prototype CO2 and humidity control system

A regenerable CO2 and humidity control system is presently being developed for potential use on shuttle as an alternative to the baseline lithium hydroxide system. The system utilizes a sorbent material (designated HS-C) to adsorb CO2 and the latent heat load from the cabin atmosphere and desorb the CO2 and water vapor overboard when exposed to a space vacuum, thus reducing the overall vehicle heat rejection load. Continuous operation is achieved by utilizing two beds which are alternatively cycled between adsorption and desorption. The HS-C material process was verified. Design concepts for the auxiliary components for the HS-C prototype system were generated. Performance testing verified system effectiveness in controlling CO2 partial pressure and humidity.

Rudy, K. M.↗
NASA NTRSDate not supplied

Next Generation Durability and Damage Tolerance to Support Certification of Flight Hardware

Durability and Damage Tolerance (D&DT), as currently applied to flight hardware throughout the Agency, is based on continuum and similitude assumptions and does not consider local material properties, environments and responses. These limitations impact our ability to support certification of new component designs, material systems and manufacturing approaches. The primary concern in this Engineering R&A plan is related to the imminent proliferation of parts produced by additive manufacturing (AM). These parts are being advocated by NASA’s suppliers for use on a myriad of flight hardware because of their design flexibility and cost advantages. AM provides opportunities to reduce part counts through complex geometry and reduce manufacturing costs of low volume parts. However, AM materials have some notable metallurgical and microstructural differences compared to traditionally fabricated materials. One of the challenges for the acceptance of AM is the greater tendency for a deleterious defect state, most commonly in the form of porosity, to exist in AM parts. Though this defect state is typically reduced through a hot isostatic pressing (HIP) processing step, the structural performance risks associated with the remaining defects and HIP-healed features is not known. Potential fracture control issues that must be resolved stem from the real possibility that an unhealed defect or a closed defect with less than pristine strength remains at a fracture critical location after HIP. As a result, NASA and the entire aerospace community have been confronted with the need to develop a robust and relevant certification methodology to enable safe implementation of these components. The present work is an important step toward positioning NASA to credibly respond to vendors’ push to implement this new AM materials technology by improving our understanding of AM processing and performance and transitioning that research-based understanding to next-generation engineering capabilities.

Edward H Glaessgen↗
DOE OSTI2026

Hidden Spin-Valley Locking Stabilizes Nanosecond Spin Polarization in 2D Perovskites

Room-temperature spin control in semiconductors is fundamental to spin-optoelectronics. Although inversion symmetry breaking offers one path for spin control in semiconductors, strong spin dephasing at elevated temperatures remains a persistent limitation. Hidden spin polarization without global symmetry breaking provides another promising material design strategy, but robust spin stabilization from this effect has yet to be experimentally realized. Here we show spin stabilization at room temperature in two-dimensional hybrid organic-inorganic perovskites through hidden spin-valley locking. We use functional non-primary ammonium cations to induce symmetry-breaking distortions in the metal-halide layers, producing giant local spin splitting while preserving global inversion symmetry. Time-resolved circular dichroism measurements reveal optically generated spin-polarized carriers that persist for 686 ps in (AzOH)2PbI4 and 4.7 ns in the lead-free analogue (AzOH)2SnI4 at room temperature, without an external magnetic field. First-principles calculations suggest that these long lifetimes arise from hidden spin valleys, enhanced dielectric screening and reduced spin-orbit-induced scattering in the Sn-based compound. Our design paradigm unlocks inversion-symmetric semiconductors with ultralong spin lifetimes, broadening the materials options for light-driven spin control.

14 SOLAR ENERGY↗
NASA NTRS1996

Experiments in Natural and Synthetic Dental Materials: A Mouthful of Experiments

The objectives of these experiments are to show that the area of biomaterials, especially dental materials (natural and synthetic), contain all of the elements of good and bad design, with the caveat that a person's health is directly involved. The students learn the process of designing materials for the complex interactions in the oral cavity, analyze those already used, and suggest possible solutions to the problems involved with present technology. The N.I.O.S.H. Handbook is used extensively by the students and judgement calls are made, even without extensive biology education.

Masi, James V.↗
NASA NTRSDate not supplied

Enhanced Quantification of 3D Woven Composites Via Fourier Analysis and Structure Tensors Applied to CT Scans

NASA's development of advanced 3D woven carbon composites, such as HEEET and 3MDCP, represents a significant leap in materials designed to endure extreme atmospheric entry conditions. However, the internal complexity of these materials introduces a variety of structures and novel defect types. Traditional characterization methods rely on labor-intensive, manual examination of X-ray CT scans, demanding considerable time and expertise. This process, while thorough, is not sustainable for large-scale analysis and defect tracking. Addressing this critical challenge, this presentation introduces an innovative suite of automated tools and analytical techniques, pioneered by the Mars Sample Return (MSR) Earth Entry System (EES) Thermal Protection System (TPS) project. These advancements offer a more efficient and accurate approach for quantifying CT data and characterizing the defects of 3D woven composites.

Magnus Haw↗
NASA NTRSDate not supplied

Quantitative Characterization of 3D Woven Composites Via Fourier Analysis and Structure Tensors Applied to CT Scans

NASA's development of advanced 3D woven carbon composites, such as HEEET and 3MDCP, represents a significant leap in materials designed to endure extreme atmospheric entry conditions. However, the internal complexity of these materials introduces a variety of structures and novel defect types. Traditional characterization methods rely on labor-intensive, manual examination of X-ray CT scans, demanding considerable time and expertise. This process, while thorough, is not sustainable for large-scale analysis and defect tracking. Addressing this critical challenge, this presentation introduces an innovative suite of automated tools and analytical techniques, pioneered by the Mars Sample Return (MSR) Earth Entry System (EES) Thermal Protection System (TPS) project. These advancements offer a more efficient and accurate approach for quantifying CT data and characterizing the defects of 3D woven composites.

3D Woven↗
DOE OSTI2022

New challenges in oxygen reduction catalysis: a consortium retrospective to inform future research

In this perspective, we highlight results of a research consortium devoted to advancing understanding of oxygen reduction reaction (ORR) catalysis as a means to inform fuel cell science. We demonstrate how targeted collaborations between different institutions from academic, national lab, and industry backgrounds and different scientific disciplines like theory, experiment, and characterization can yield unique insights into fuel cell catalysts. We comment on such insights into material designs for platinum-group-metal alloys, transition metal oxides, and non-traditional materials including metal–organic frameworks; systems that have served as the foundational building blocks for our consortium. We also motivate a renewed focus on catalyst durability in light of emerging technological requirements and paths forward in understanding in situ and operando electrochemical stability. Lastly, we describe new frontiers ORR research can take and how emerging artificial intelligence tools can assist researchers in capturing data, selecting new experiments, and guiding characterization to accelerate the design and discovery of fuel cell catalysts. A main goal of sharing this perspective is to discuss the rationale for our future research plans based on our consortium work. However, we also hope to illustrate both the potential impact of a collaborative strategy with the hopes of inspiring a higher degree of Industry-Academia-National Laboratory collaboration and encourage other centers and consortiums to distill and share their findings in a similar perspective-type article. Together we hope to enable the fuel cell research community to engage in a discussion of strategies for research and accelerated development of catalysts with improved activity and stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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↗
DOE OSTI2025

Accurate and uncertainty-aware multi-task prediction of HEA properties using prior-guided deep Gaussian processes

Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys (HEAs), especially when integrating computational predictions with sparse experimental observations. This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes (cGP), Deep Gaussian processes (DGP), encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting (XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V. We specifically assess their capabilities in predicting correlated material properties, including yield strength, hardness, modulus, ultimate tensile strength, elongation, and average hardness under dynamic/quasi-static conditions, alongside auxiliary computational properties. The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic, heterotopic, and incomplete data commonly encountered in materials science. Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperform other surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge. This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and data-efficient materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Automated qualification data tool for high temperature metallic materials

This report describes a framework for storing, processing, and displaying qualification data for high temperature mechanical properties. The framework automates the process of generating design data from mechanical test results, for example for a data qualification report for the ASME Boiler \& Pressure Vessel Code. The framework has three parts: a data storage model with common formats for several types of typical mechanical property tests, a backend based on the \pycreep Python library for correlating and extrapolating the data to generate design material properties and allowable stresses, and a demonstration user interface for displaying, sorting, and filtering the data and exploring different options for modeling the design mechanical properties. The report discusses the options available for data processing, with illustrations from real test data on Alloy 617, Alloy 709, Alloy 740H, and Laser-Powder Bed Fusion 316H. The framework is complete for ASME type data analysis and will be used to store test data generated by the Department of Energy, Office of Nuclear Energy, Advanced Materials and Manufacturing Technologies sponsored qualification programs. Future work could extend the tool to other types of material properties and/or expand the demo user interface to make it accessible across the AMMT program.

36 MATERIALS SCIENCE↗
DOE OSTI2022

(111) Faceted Metal Oxides: A Review of Synthetic Methods

Material design and synthesis have made tremendous impacts in the scientific community by unleashing a material's true potential via enhanced properties and applications. Over the years, advanced synthetic strategies have emerged and have been expanded to not only control the size and shape of nanoparticles but also to control the preferential growth of surface facets, paving the way for new materials with facet-dependent properties. Metal oxide (111) facets as compared to their potentially more stable counterpart facets (e.g., (100), (110)) have recently exhibited enriched chemical properties owing to their unique surface arrangement. As a result, metal oxide (111) faceted surfaces have been used in applications such as catalysis, sorbents, batteries, etc. This work aims to provide a perspective on the synthetic processes utilized to expose (111) surfaces and the governing factors/synthetic parameters that expose them across various metal oxides of different crystal structures as well as some of their applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Magnetic Properties Tuning via Broad Range Site Deficiency in Square Net Material UCu x Bi 2

HfCuSi 2 -type pnictogen compounds have recently been shown to be a versatile platform for designing materials with topologically nontrivial band structures. However, these phases require strict control over the electron count to tune the Fermi level, which can only be achieved in compositions with A 2+ M 2+ Pn 2 and A 3+ M + Pn 2 (A = lanthanides, M = transition metals, Pn = pnictogens P–Bi) charge distribution. While such lanthanide compounds have been thoroughly studied as candidate magnetic topological materials, their heavy element analogs with uranium and bismuth remain largely underexplored. In this report, we present the synthesis of UCu x Bi 2 single crystals and study their magnetic properties. Detailed structural analysis revealed that flux-grown crystals always form as a site-deficient UCu x Bi 2 composition, where x varies between 0.20 and 0.64. Magnetic property measurements revealed a dependence of the magnetic coupling on the Cu site deficiency, linearly changing the Néel temperature from 51 K for UCu 0.60 Bi 2 to 118 K for UCu 0.30 Bi 2 . Moreover, higher Cu concentration promotes a metamagnetic transition in highly magnetically anisotropic UCu 0.60 Bi 2 single crystals. We show that DFT calculations can successfully model site deficiency in the UCu x Sb 2 and UCu x Bi 2 systems. This work paves the way toward using the site deficiency to tune the Fermi level in more ubiquitous A 3+ M 2+ x Pn 2 phases, which previously have not been considered topological candidate materials due to unfavorable electron count.

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

Accelerating the discovery of novel magnetic materials using machine learning–guided adaptive feedback

Magnetic materials are essential for energy generation and information devices, and they play an important role in advanced technologies and green energy economies. Currently, the most widely used magnets contain rare earth (RE) elements. An outstanding challenge of notable scientific interest is the discovery and synthesis of novel magnetic materials without RE elements that meet the performance and cost goals for advanced electromagnetic devices. Here, we report our discovery and synthesis of an RE-free magnetic compound, Fe 3 CoB 2 , through an efficient feedback framework by integrating machine learning (ML), an adaptive genetic algorithm, first-principles calculations, and experimental synthesis. Magnetic measurements show that Fe 3 CoB 2 exhibits a high magnetic anisotropy ( K 1 = 1.2 MJ/m 3 ) and saturation magnetic polarization ( J s = 1.39 T), which is suitable for RE-free permanent-magnet applications. Our ML-guided approach presents a promising paradigm for efficient materials design and discovery and can also be applied to the search for other functional materials.

36 MATERIALS SCIENCE↗