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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Immersive Visualization for Scientific Data Analysis

We will present the use of immersive visualization at the National Renewable Energy Laboratory (NREL), showcasing how immersive visualization is advancing scientific research and engineering practices and transforming our day-to-day operations. We are leveraging immersive visualization to support scientific discovery and engineering in various domains, including material design, computational fluid dynamics, immersive analytics, grid modernization, digital twins, and situated visualization. We have observed several benefits across four key areas: enhanced spatial judgments, improved understanding through interaction, increased capacity to embed high-dimensional data, and improved collaboration.

immersive analytics↗
DOE OSTI2025

DEVELOPMENT OF INEXPENSIVE HIGH TEMPERATURE NITI-BASED SHAPE MEMORY ALLOYS FOR POWDER BED ADDITIVE MANUFACTURING

NiTi and NiTi-based Shape Memory Alloys (SMA) exhibit a reversible solid-state phase transformation from martensite to austenite driven by thermal energy. High temperature (Mf>100°C) SMAs are martensite at room temperature and can be fabricated into solid-state actuators that return to a pre-programmed shape against a designed load after heating to transformation threshold. Reactive as-fabricated additively manufactured parts (4-D printing) is the current state of the art in manufacturing of SMAs but requires compositions compliant to rapid solidification. Existing actuator designs are developed from commercially available, highly investigated material compositions. However, existing high temperature high performance (high actuation strain, low thermal hysteresis) shape memory alloys contain significant (>10% at.) portions of high-cost Platinum Group Metals (PGMs). It is of significant scientific interest to investigate material compositions that are peer performing or superior to PGMs whose constituent elements represent a significant cost savings. Shape memory alloy properties vary significantly with small (0.1% at.) compositional changes making robust investigative sample sets very large. Computational material design can be deployed to shrink the compositional space of possible alloy combinations and reduce the experimental load in material discovery. Investigating shape memory effect (SME) and validating process additive process parameters for a single novel composition is cost intensive in both time and consumed materials. Additionally, sub-optimal processing, oxygen, or solidification rate sensitivity could render additively manufacturing specimens without micro, macro cracks, or significant chemical variance impossible. Unfortunately, such failure susceptibility cannot be simulated. Therefore, a research pathway to validate novel shape memory alloy compositions for powder bed fusion additive manufacturing without the need for powdered feedstock is also proposed. This research investigates novel high temperature shape memory alloys for actuators without platinum group alloying elements to discover one that could be commercially viable as an additive manufacturing feedstock.

Sundermann, Tayler↗
DOE OSTI2020

Computational thermodynamics and its applications

Thermodynamics is a science concerning the state of a system, whether it is stable, metastable or un- stable, when interacting with the surroundings. In this overview, fundamentals of thermodynamics are briefly reviewed through the combination of first and second laws of thermodynamics for open and nonequilibrium systems to demonstrate that the reversible equilibrium and irreversible nonequilibrium thermodynamics can be integrated to enhance the power and utilities of thermodynamics. The recent progresses in computational thermodynamics, the remaining challenges, and potential impacts in broad scientific fields are discussed here. It is shown that computational thermodynamics enables the modeling of thermodynamics of a state as a function of both external and internal variables and quantitative calculations of a broad range of properties of a multicomponent system in terms of first and second derivatives of energy, including not only equilibrium states when there are no driving forces for any internal processes and but also non-equilibrium states with driving forces for internal processes. Consequently, external constraints such as fixed strain and internal degree of freedoms such as ordering and defects can be described in a coherent framework and applied to materials design. Furthermore, two important but largely overlooked aspects in thermodynamics will be discussed, i.e. the rigorous application of statistical thermodynamics with the probability of configurations and their contributions to system properties, and the applications of second derivatives of energy with respect to either two extensive variables or two potentials or a mixture of them in terms of understanding and predicting emergent behaviors, critical phenomena, kinetic coefficients, and mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Designing alloys with process-mapping AI pre-trained on empirical knowledge

<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Accelerated materials design should match the recent trends in the product development cycles. Materials data analytics can be used to significantly shorten development time of specialized alloys needed for next generation energy applications. However, it faces a challenge of scarce data available for training ML models. Incorporation of the domain knowledge into deep-learning graph structure via fuzzy pre-training and causal process imitation presents a viable approach to developing accurate data-driven models and reliable alloy design tools, with limited datasets. Artificial Intelligence (AI) was used in this study to incorporate such knowledge in the domain-specific computational tool, pyroMind. The tool provides not only novel design ideas but also their interpretation via physics and engineering concepts.</span>

Romanov, Vyacheslav↗
DOE OSTI2025

Two-Dimensional Silk Crystal Films as Matrix Layer for High-Performance Microelectronics

This study explores a bio-inspired approach for memristive devices by combining Keggin-type polyoxometalates (POMs)-[SiW 12 O 40 ] 4 (POM-T) and [PW 12 O 40 ] 3 (POM-P), with silk fibroin (SF) to create 2D SF–POM layers on highly ordered pyrolytic graphite (HOPG) as resistive switching layers for memristors. We propose that the ordered SF layer template 0D POMs facilitate the formation of conductive filaments, thereby enhancing the variability of the manufactured memristors. AFM analysis revealed that both SF and SF–POM layers shared similar morphologies, while SF–POM–T formed larger aggregates, likely due to the stronger acidity of POM-T, which probably caused SF to aggregate and alter its secondary structure. Scanning Kelvin probe microscopy (SKPM) revealed that POMs reduced the contact potential difference of HOPG, resulting in lower work functions. Compared to an SF device, the SF–POM–P device showed improved memristive behavior, with a larger current gap and good repeatability over multiple sweeps; whereas the SF–POM–T device did not exhibit memristor activity, likely due to acidity-induced disruption of the SF template’s order and CF formation. More importantly, SF–POM–P devices also demonstrated programmable memristive states. Finally, combining simulation-driven memristor modeling, we showcase a co-design workflow for advancing bioinspired memristors through new materials design, synthesis, and device modeling and development.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Designing artificial two-dimensional landscapes via atomic-layer substitution

Significance Manipulating materials with atomic-scale precision is essential for the development of a next-generation material design toolbox. Tremendous efforts have been made to advance the compositional, structural, and spatial accuracy of material deposition and patterning. Here, we presented a new reaction pathway to implement the conversions of two-dimensional materials within the atomic-layer thickness at room temperature for electrical dipole manipulation. Not only could various Janus monolayer transition metal dichalcogenides with vertical dipole be realized, but also some heterostructures, including the dipole-nondipole heterostructures (MoS 2 -MoSSe) and multiheterostructures (MoS 2 -MoSSe-MoSeS-MoSe 2 ) within the same monolayer host structure are developed, in which the dipoles can be selectively patterned to be zero (MoS 2 , MoSe 2 ), positive (MoSSe), and negative (MoSeS).

Guo, Yunfan↗
DOE OSTI2025

Artificial intelligence for advanced functional materials: exploring current and future directions

This perspective addresses the topic of harnessing the tools of artificial intelligence (AI) for boosting innovation in functional materials design and engineering as well as discovering new materials for targeted applications in energy storage, biomedicine, composites, nanoelectronics or quantum technologies. It gives a current view of experts in the field, insisting on challenges and opportunities provided by the development of large materials databases, novel schemes for implementing AI into materials production and characterization as well as progress in the quest of simulating physical and chemical properties of realistic atomic models reaching the trillion atoms scale and with near ab initio accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Modeling kinetic effects of charged vacancies on electromechanical responses of ferroelectrics: Rayleighian approach

Understanding the time-dependent effects of charged vacancies on the electromechanical responses of materials is at the forefront of research for designing materials exhibiting metal-insulator transitions and memristive behavior. A Rayleighian approach is used to develop a model for studying the nonlinear kinetics of the reaction leading to generation of vacancies and electrons via the dissociation of vacancy-electron pairs. Also, diffusion and elastic effects of charged vacancies are considered to model polarization-electric potential and strain-electric potential hysteresis loops. The model captures multiphysics phenomena by introducing couplings among polarization, the electric potential, stress, strain, and concentrations of charged (multivalent) vacancies and electrons (treated as classical negatively charged particles), where the concentrations can vary due to association-dissociation reactions. A derivation of coupled time-dependent equations based on the Rayleighian approach is presented. Three limiting cases of the governing equations are considered, highlighting the effects of (1) nonlinear reaction kinetics on the generation of charged vacancies and electrons, (2) Vegard's law (i.e., the concentration-dependent local strain) on asymmetric strain-electric potential relations, and (3) coupling between a fast component and the slow component of the net polarization on the polarization-electric-field relations. The Rayleighian approach discussed in this work should pave the way for developing a multiscale modeling framework in a thermodynamically consistent manner while capturing multiphysics phenomena in ferroelectric materials. Published by the American Physical Society 2025

Kumar, Rajeev (ORCID:0000000194943488)↗
DOE OSTI2024

Analysis of the ASME Code Rules for Subsection III-5-HHB (Composite Materials) for Current HTR Design Requirements

This document includes the critical analysis review of the American Society of Mechanical Engineers (ASME) Section III Division 5 Subsection HH Subpart B (HHB), including Mandatory Appendices, that was published in 2023. In the context of this document, reference to “the code” is specific to this subsection unless otherwise specified. A specific composites task group within the ASME Nonmetallic Design and Materials Working Group, with the support of external experts, was established to perform a gap analysis review. The significant findings are summarized here. The committee response with suggested action items are detailed in the body of the report.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The Role of Interface in Additively Manufactured Interpenetrating Composites

Additively Manufactured Interpenetrating Composites (AMIPCs) are a relatively new metal-metal chain composite in development for use in high energy absorption systems. In this system, reinforcing phase of additively manufactured continuous lattice configurations 316L austenitic stainless-steel is in melt infiltrated with a matrix phase of A356 aluminum-silicon casting alloy. Measurements and observations of this material system have shown that weakly bonded or open/porous interface between the reinforcement and matrix phases exhibits dramatically different mechanical properties of AMIPCs, which is not currently well understood. In this work, Finite Element Models (FEM) are used to model the effects of interfaces between the composite phases. Mechanical tensile tests measurements of various composite volume fractions and varying degrees of casting infiltration are also examined and used to show consistency with the FEM results. The outcome provides insight into material design criteria and performance predictions for new hybrid material systems with exceptional damage tolerance.

Allen, Jason↗
DOE OSTI2024

Machine learning-guided design, synthesis, and characterization of atomically dispersed electrocatalysts

The recent integration of machine learning into materials design has revolutionized the understanding of structure–property relationships and optimization of material properties beyond the trial-and-error paradigm. On one hand, machine learning has significantly accelerated the development of atomically dispersed metal-nitrogen-carbon (M-N-C) electrocatalysts, which traditionally heavily relied on heuristic approaches. On the other hand, the primary challenge of leveraging machine learning to expedite M-N-C materials discovery lies in the cost associated with data collection. Here, we review recent machine learning integration strategies for M-N-C catalyst development, including discussions on the typical algorithms such as symbolic regression and convolutional neural networks employed for the theoretical design, synthesis optimization via active learning, and advanced microscopy characterization. Subsequently, we provide our perspective on potential near-future directions for furthering machine learning-assisted development of new M-N-C catalysts and elucidating the complex physicochemical mechanisms governing the selectivity, activity, and durability in this class of materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Interparticle Characterization of Mechanical Biomass Particle-Particle and Particle-Wall Interactions

The biomass materials industry faces significant challenges in managing material variability and its impact on storage and handling systems. Physical properties such as moisture content, particle size, and density fluctuate considerably, leading to operational issues like bridging and ratholing that disrupt material flow. These variations create a complex cascade effect throughout the process chain, affecting transportation, storage, and conversion processes. The economic consequences of this variability manifest in increased operational costs, maintenance requirements, and system downtime. Environmental factors further complicate the situation, as weather conditions and seasonal availability influence material properties and system performance. Engineers employ specialized equipment design, material characterization protocols, and pre-processing steps like size reduction and homogenization to address these challenges. A critical knowledge gap exists between continuous-level constitutive models and particle-scale behavior. This project developed a novel device to quantify interparticle mechanics between biomass particles, measuring friction and adhesion forces between particles and wall materials. The research focused on corn stover and southern pine forest residue, creating a comprehensive database of particle interactions. This breakthrough enables direct application in particle-based computational modeling, advancing the field's understanding of biomass handling characteristics and supporting the development of more reliable and efficient storage and handling systems. The project's outcomes contribute significantly to understanding biomass's mechanical and flow characteristics, particularly how variability at the particle level affects larger-scale handling operations. This knowledge is crucial for engineering feedstock supply systems that consistently meet quality and cost specifications for various conversion processes. The innovative experimental setup developed through this research represents a significant advancement in biomass characterization methodology. Providing precise measurements of particle-level interactions establishes a foundation for more accurate predictive modeling of bulk material behavior. This enhanced understanding of fundamental particle mechanics enables engineers to anticipate better and address handling challenges before they manifest in full-scale operations. This research opens new avenues for optimizing biomass handling systems through data-driven design approaches. The comprehensive database of particle interactions serves as a valuable resource for future research and development efforts, potentially leading to more efficient and cost-effective biomass processing solutions. This advancement in particle-level mechanics could revolutionize how biomass handling systems are designed and operated, contributing to more sustainable and reliable renewable energy production.

09 BIOMASS FUELS↗
DOE OSTI2023

High-throughput ab initio design of atomic interfaces using InterMatch

Forming a hetero-interface is a materials-design strategy that can access an astronomically large phase space. However, the immense phase space necessitates a high-throughput approach for an optimal interface design. Here we introduce a high-throughput computational framework, InterMatch, for efficiently predicting charge transfer, strain, and superlattice structure of an interface by leveraging the databases of individual bulk materials. Specifically, the algorithm reads in the lattice vectors, density of states, and the stiffness tensors for each material in their isolated form from the Materials Project. From these bulk properties, InterMatch estimates the interfacial properties. We benchmark InterMatch predictions for the charge transfer against experimental measurements and supercell density-functional theory calculations. We then use InterMatch to predict promising interface candidates for doping transition metal dichalcogenide MoSe 2 . Finally, we explain experimental observation of factor of 10 variation in the supercell periodicity within a few microns in graphene/α-RuCl 3 by exploring low energy superlattice structures as a function of twist angle using InterMatch. We anticipate our open-source InterMatch algorithm accelerating and guiding ever-growing interfacial design efforts. Moreover, the interface database resulting from the InterMatch searches presented in this paper can be readily accessed online.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Accelerating Materials Discovery: Artificial Intelligence for Sustainable, High-Performance Polymers

PolyID enables the discovery of polymers with advanced performance and greater sustainability while reducing material development timelines. The material design space is immense and cannot be reasonably probed using an Edisionian approach. High-throughput property prediction, enabled by artificial intelligence provides a hypothesis driven approach for down selection of candidate polymers to pursue experimentally. To aid experimentalists in the down selection of material targets this high-throughput, machine learning-based tool is capable of predicting polymer properties simply from molecular structures. Currently, transport, thermal, and mechanical properties across 7 polymer class (polyamides, polyesters, polycarbonates, polyimides, polyolefins, polyacrylates, and polyurethanes) can be predicted, and the PolyID platform has been flexibly designed so new materials and properties can be added.

artificial intelligence↗
DOE OSTI2024

Perspective on Lewis Acid‐Base Interactions in Emerging Batteries

Lewis acid-base interactions are common in chemical processes presented in diverse applications, such as synthesis, catalysis, batteries, semiconductors, and solar cells. The Lewis acid-base interactions allow precise tuning of material properties from the molecular level to more aggregated and organized structures. This review will focus on the origin, development, and prospects of applying Lewis acid-base interactions for the materials design and mechanism understanding in the advancement of battery materials and chemistries. The covered topics relate to aqueous batteries, lithium-ion batteries, solid-state batteries, alkali metal-sulfur batteries, and alkali metal-oxygen batteries. In this review, the Lewis acid-base theories will be first introduced. Thereafter the application strategies for Lewis acid-base interactions in solid-state and liquid-based batteries will be introduced from the aspects of liquid electrolyte, solid polymer electrolyte, metal anodes, and high-capacity cathodes. The underlying mechanism is highlighted in regard to ion transport, electrochemical stability, mechanical property, reaction kinetics, dendrite growth, corrosion, and so on. Last but not least, perspectives on the future directions related to Lewis acid-base interactions for next-generation batteries are like to be shared.

Lewis acid-base interactions↗
DOE OSTI2022

Phase Stability Through Machine Learning

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

36 MATERIALS SCIENCE↗
DOE OSTI2021

OPTIMADE, an API for exchanging materials data

Abstract The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗