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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Computer-aided design of polymers and composites

This book on computer-aided design of polymers and composites introduces and discusses the subject from the viewpoint of atomic and molecular models. Thus, the origins of stiffness, strength, extensibility, and fracture toughness in composite materials can be analyzed directly in terms of chemical composition and molecular structure. Aspects of polymer composite reliability are considered along with characterization techniques for composite reliability, relations between atomic and molecular properties, computer aided design and manufacture, polymer CAD/CAM models, and composite CAD/CAM models. Attention is given to multiphase structural adhesives, fibrous composite reliability, metal joint reliability, polymer physical states and transitions, chemical quality assurance, processability testing, cure monitoring and management, nondestructive evaluation (NDE), surface NDE, elementary properties, ionic-covalent bonding, molecular analysis, acid-base interactions, the manufacturing science, and peel mechanics.

Kaelble, D. H.↗
DOE OSTI2020

Neural Network Molecular Dynamics at Scale

Neural network molecular dynamics (NNMD) simulations could revolutionize atomistic modeling of materials with quantum-mechanical accuracy at a fraction of computational cost. However, popular NNMD frameworks are generally implemented for a single computing node, and conventional energy-based NN models still suffer from large time-to-solution (T2S), prohibiting the application of NNMD to challenging materials simulations encompassing large spatiotemporal scales. Consequently, no leadership-scale NNMD simulation has thus far been reported. Here, we present a scalable parallel NNMD software (RXMD-NN) based on our scalable reactive molecular dynamics simulation engine named RXMD. RXMD-NN has achieved high scalability up to 786,432 IBM BlueGene/Q cores involving 1.7 billion atoms. Furthermore, we have achieved 4.6-fold reduction of T2S by using a novel network that directly predicts atomic forces from feature vectors. Reduced T2S has for the first time allowed the study of large-scale off-stoichiometry effect in a widely used phase change material, Ge 2 Se 2 Te 5 , thereby resolving its "firstsharp diffraction peak mystery".

fist sharp diffraction peak↗
DOE OSTI2024

Physics-based modeling and data analytics [Slides]

This presentation contains a summary of ongoing work within the physics-based modeling and data analytics work package within the Nuclear Materials Discovery and Qualification initiative (NMDQi). Topics include work on MOOSE-based crystal plasticity, molecular dynamics modeling of recombination in metals and alloys, the MOOSE Stochastic Tools Module, and machine learning and atomistic modeling to predict thermo-kinetic properties of nuclear structural materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

PDB ‐101: Educational resources supporting molecular explorations through biology and medicine

Abstract The Protein Data Bank (PDB) archive is a rich source of information in the form of atomic‐level three‐dimensional (3D) structures of biomolecules experimentally determined using macromolecular crystallography, nuclear magnetic resonance (NMR) spectroscopy, and electron microscopy (3DEM). Originally established in 1971 as a resource for protein crystallographers to freely exchange data, today PDB data drive research and education across scientific disciplines. In 2011, the online portal PDB‐101 was launched to support teachers, students, and the general public in PDB archive exploration ( pdb101.rcsb.org ). Maintained by the Research Collaboratory for Structural Bioinformatics PDB, PDB‐101 aims to help train the next generation of PDB users and to promote the overall importance of structural biology and protein science to nonexperts. Regularly published features include the highly popular Molecule of the Month series, 3D model activities, molecular animation videos, and educational curricula. Materials are organized into various categories (Health and Disease, Molecules of Life, Biotech and Nanotech, and Structures and Structure Determination) and searchable by keyword. A biennial health focus frames new resource creation and provides topics for annual video challenges for high school students. Web analytics document that PDB‐101 materials relating to fundamental topics (e.g., hemoglobin, catalase) are highly accessed year‐on‐year. In addition, PDB‐101 materials created in response to topical health matters (e.g., Zika, measles, coronavirus) are well received. PDB‐101 shows how learning about the diverse shapes and functions of PDB structures promotes understanding of all aspects of biology, from the central dogma of biology to health and disease to biological energy.

Zardecki, Christine↗
DOE OSTI2022

Ga + -Chabazite Zeolite: A Highly Selective Catalyst for Nonoxidative Propane Dehydrogenation

Ga-chabazite zeolites (Ga-CHA) have been found to efficiently catalyze propane dehydrogenation with high propylene selectivity (96%). In situ FTIR spectroscopy and pulse titrations are employed to determine that upon reduction, surface Ga 2 O 3 is reduced and diffuses into the zeolite pores, displacing the Brønsted acid sites (BAS) and forming extra-framework Ga + sites. This isolated Ga + site reacts reversibly with H 2 to form GaHx (2034 cm -1 ) with an enthalpy of formation of ~ -51.2 kJ·mol -1 , a result supported by Density functional theory (DFT) calculations. The initial C 3 H 6 dehydrogenation rates decrease rapidly (40%) during the first 100 min and then decline slowly afterward, while the C 3 H 6 selectivity is stable at ~ 96%. The reduction in the reaction rate is correlated with the formation of polycyclic aromatics inside the zeolite (using UV-vis spectroscopy) indicating that the accumulation of polycyclic aromatics is the main cause of the deactivation. The carbon species formed can be easily oxidized at 600 °C with complete recovery of the PDH catalytic properties. The correlations between GaH x vs. Ga/Al ratio, and PDH rates vs. Ga/Al ratio show that extra-framework Ga + is the active center catalyzing propane dehydrogenation. The higher reaction rate on Ga + than In + in CHA zeolites, by a factor of 43, is the result of differences in the stabilization of the transition state due to the higher stability of Ga 3+ vs. In 3+ . The uniformity of the Ga + sites in this material makes it an excellent model for the molecular understanding of metal cation exchanged hydrocarbon interactions in zeolites.

10 SYNTHETIC FUELS↗
NASA NTRS1986

Electron-radiation effects on the ac and dc electrical properties and unpaired electron densities of three aerospace polymers

The effects of gigarad-level total absorbed doses from 1-MeV electrons on the post-irradiation alternating-current (ac) and direct-current (dc) electrical properties and the unpaired electron densities have been studied for Kapton, Ultem, and Mylar. The unpaired electron densities (determined from electron paramagnetic resonance spectroscopy) and the dc electrical conductivities of the irradiated materials were monitored as functions of time following the exposures to determine their decay characteristics at room temperature. The elevated-temperature ac electrical dissipations of the Ultem and Mylar were affected by the radiation. The dc conductivity of the Kapton increased by five orders of magnitude, while the dc conductivities of the Ultem and Mylar increased by less than an order of magnitude, due to the radiation. The observed radiation-generated changes in the ac electrical dissipations are explained in terms of known radiation-generated changes in the molecular structures of the three materials. A preliminary model relating the dc electrical conductivity and the unpaired electron density in the Kapton is proposed.

Long, Sheila Ann T.↗
NASA NTRS1992

On the molecular complexity of the hot cores in Orion A - Grain surface chemistry as 'The last refuge of the scoundrel'

We have modeled the gas phase chemistry of warm molecular material around protostars that is seeded with evaporating grain mantles. We show that the release of simple molecules into the gas drives ion-molecule and neutral chemistries which can account for many of the complex O-bearing and N-bearing molecules observed in hot cores. Initial grain mantle components and secondary product molecules are identified, and the observational consequences are discussed.

Charnley, S. B.↗
DOE OSTI2022

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Abstract Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing (i) reduction of data loading time up to 4.2 times compared with a conventional method and (ii) linear scaling performance for training up to 1024 GPUs on both Summit and Perlmutter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Regularized machine learning on molecular graph model explains systematic error in DFT enthalpies

Abstract A major goal of materials research is the discovery of novel and efficient heterogeneous catalysts for various chemical processes. In such studies, the candidate catalyst material is modeled using tens to thousands of chemical species and elementary reactions. Density Functional Theory (DFT) is widely used to calculate the thermochemistry of these species which might be surface species or gas-phase molecules. The use of an approximate exchange correlation functional in the DFT framework introduces an important source of error in such models. This is especially true in the calculation of gas phase molecules whose thermochemistry is calculated using the same planewave basis set as the rest of the surface mechanism. Unfortunately, the nature and magnitude of these errors is unknown for most practical molecules. Here, we investigate the error in the enthalpy of formation for 1676 gaseous species using two different DFT levels of theory and the ‘ground truth values’ obtained from the NIST database. We featurize molecules using graph theory. We use a regularized algorithm to discover a sparse model of the error and identify important molecular fragments that drive this error. The model is robust to rigorous statistical tests and is used to correct DFT thermochemistry, achieving more than an order of magnitude improvement.

36 MATERIALS SCIENCE↗
DOE OSTI2025

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery

Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.

Computer Science↗
DOE OSTI2023

Autonomous Molecular Structure Imaging with High-Resolution Atomic Force Microscopy for Molecular Mixture Discovery

Due to its single-molecule sensitivity, high-resolution atomic force microscopy (HR-AFM) has proved to be a valuable and uniquely advantageous tool to study complex molecular mixtures, which hold promise for developing clean energy and achieving environmental sustainability. However, significant challenges remain to achieve the full potential of the sophisticated and time-consuming experiments. Automation combined with machine learning (ML) and artificial intelligence (AI) is key to overcoming these challenges. Here we present Auto-HR-AFM, an AI tool to automatically collect HR-AFM images of petroleum-based mixtures. In this study, we trained an instance segmentation model to teach Auto-HR-AFM how to recognize features in HR-AFM images. Auto-HR-AFM then uses that information to optimize the imaging by adjusting the probe-molecule distance for each molecule in the run. Auto-HR-AFM is the initial tool that will lead to fully automated scanning probe microscopy (SPM) experiments, from start to finish. This automation will allow SPM to become a mainstream characterization technique for complex mixtures, an otherwise unattainable target.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Predictive coarse-grained (CG) modeling of morphologies in polymer nanocomposites with specific and directional intermolecular interactions (Final Report)

The overarching goal of the proposed work was to develop predictive models for investigating structure and dynamics in soft materials with chemistries that have specific and directional molecular interactions. The motivation behind studying materials with specific and directional interactions lies in the many desirable features these interactions provide when designing novel soft materials. Soft materials with specific and direction interactions (such as hydrogen bonds or H-bonds) can have a) thermally reversible phase behavior with different functions with varying temperature, b) precisely tuned nanostructure with desirable geometries that afford unique physical properties (e.g., color response, mechanical properties) and c) well-mixed/blended morphologies that are useful for variety of applications in energy field (e.g., materials for batteries require use of blended polymers where one domain gives superior mechanical properties and one domain promotes electrical conduction). Notably, biology makes extensive use of specific and directional interactions, in many cases based on H-bonds, to construct materials with precisely defined architectures and properties. Engineered soft materials with precisely tuned nanostructures and improved processiblity through thermoresponsive phase behavior are useful in numerous applications that are relevant to the Department of Energy (DOE) including high efficiency electronic devices, light-weight high-strength composite materials for batteries and fuel cells, and polymer membranes for separations, etc. While past computational studies have been tremendously useful in understanding molecular phenomena and guiding synthesis of new macromolecular soft materials for a wide variety of applications, the inability to capture small scale specific and directional interactions alongside macromolecular length and time scales represented a key limitation of most studies to date. Our work in this project addressed this grand challenge in computational materials chemistry, i.e., ability to model the anisotropic, directional, and specific interactions that govern the behavior of many macromolecular soft matter systems of interest, thus, has greatly expanded the predictive potential of simulations. Specifically the key outcomes were: successful development of new coarse-grained (CG) polymer models to study generic and specific polymer chemistries in which hydrogen-bonding interactions are dominant. These CG models were then used in molecular simulations to study structure and thermodynamics in polymer nanocomposites and blends; some studies were conducted in collaboration with experimentalists. We also published a perspective and a viewpoint which included some of the work we completed in this DOE project; we believe these perspective and viewpoint articles guide other researchers in the soft materials community on how to extend the computational approaches and models we have developed for the purposes of their studies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Probing the Structure and Dynamics of Fluid Mixtures in Porous Materials Through Ultrafast Vibrational Spectro-Microscopy and Many-Body Molecular Dynamics

This research program focused on the characterization of both molecular structure and dynamics of aqueous solutions in various metal-organic frameworks (MOFs) and organic polymers that have recently been proposed for applications in water treatment technologies. This was accomplished by integrating ultrafast vibrational spectroscopy and microscopy, which are sensitive to local environments, molecular orientation, and dynamical couplings, with many-body molecular dynamics (MB-MD) simulations, which enable realistic modeling of aqueous systems and porous materials under different thermodynamic conditions. Our studies allowed for identifying the physical mechanisms and characterizing the underlying molecular interactions between water and host materials that determine the adsorption and transport processes of water in various MOFs and β-cyclodextrin polymers – prototypical examples of porous materials and polymers, that have recently been proposed for technological applications in water purification. By combining bulk- and surface-sensitive, spatially resolved ultrafast vibrational spectroscopy with MB-MD simulations, it was possible to gain broad insight into the structure and mobility of water under heterogeneous confinement at various length scales. In particular, measurements of surface- and bulk-sensitive vibrational spectra at single crystal level, integrated with MB-MD simulations were used to characterize domain-specific, structure–binding affinity relationships. Besides providing specific information about the behavior of aqueous solutions in prototypical MOFs and organic polymers for potential applications in water treatment technologies, our studies also provided fundamental insights into the properties of aqueous solutions in confined heterogeneous environments, which have broad implications in many areas, ranging from heterogeneous catalysis to oil recovery, ion transport processes, and reaction-diffusion processes in crowded systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

PPW 2022 Short Course Introduction to Aerocapture, Entry, Descent, and Landing: Computational Materials

Computational materials science encompasses modeling and simulation techniques that are used to improve our understanding of materials from the atomic to macroscopic scales. This talk will focus on recent applications of computational materials science to ablative thermal protection materials, including heritage PICA and state-of-the-art woven materials like HEEET. Development of advanced molecular, microscopic, and mesoscopic models will be discussed.

phenolic↗
DOE OSTI2022

Atomistic measurement and modeling of intrinsic fracture toughness of two-dimensional materials

Quantifying the intrinsic mechanical properties of two-dimensional (2D) materials is essential to predict the long-term reliability of materials and systems in emerging applications ranging from energy to health to next-generation sensors and electronics. Currently, measurements of fracture toughness and identification of associated atomistic mechanisms remain challenging. Herein, we report an integrated experimental–computational framework in which in-situ high-resolution transmission electron microscopy (HRTEM) measurements of the intrinsic fracture energy of monolayer MoS 2 and MoSe 2 are in good agreement with atomistic model predictions based on an accurately parameterized interatomic potential. Changes in crystalline structures at the crack tip and crack edges, as observed in in-situ HRTEM crack extension tests, are properly predicted. Such a good agreement is the result of including large deformation pathways and phase transitions in the parameterization of the inter-atomic potential. The established framework emerges as a robust approach to determine the predictive capabilities of molecular dynamics models employed in the screening of 2D materials, in the spirit of the materials genome initiative. Moreover, it enables device-level predictions with superior accuracy (e.g., fatigue lifetime predictions of electro- and opto-electronic nanodevices).

Zhang, Xu↗
DOE OSTI2020

Vibrational exciton nanoimaging of phases and domains in porphyrin nanocrystals

Much of the electronic transport, photophysical, or biological functions of molecular materials emerge from intermolecular interactions and associated nanoscale structure and morphology. However, competing phases, defects, and disorder give rise to confinement and many-body localization of the associated wavefunction, disturbing the performance of the material. Here, we employ vibrational excitons as a sensitive local probe of intermolecular coupling in hyperspectral infrared scattering scanning near-field optical microscopy (IR s-SNOM) with complementary small-angle X-ray scattering to map multiscale structure from molecular coupling to long-range order. In the model organic electronic material octaethyl porphyrin ruthenium(II) carbonyl (RuOEP), we observe the evolution of competing ordered and disordered phases, in nucleation, growth, and ripening of porphyrin nanocrystals. From measurement of vibrational exciton delocalization, we identify coexistence of ordered and disordered phases in RuOEP that extend down to the molecular scale. Even when reaching a high degree of macroscopic crystallinity, identify significant local disorder with correlation lengths of only a few nanometers. This minimally invasive approach of vibrational exciton nanospectroscopy and -imaging is generally applicable to provide the molecular-level insight into photoresponse and energy transport in organic photovoltaics, electronics, or proteins.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Role of interfaces on phase formation during severe plastic deformation

The role of interfaces on phase formation during severe plastic deformation (SPD) was investigated using molecular dynamics simulations. It is found that dilute solute additions that segregate to interphase boundaries, and strengthen them, can greatly extend the solubility limits of the bounding phases at steady state, providing a novel approach to processing nanocomposite alloys by SPD. Here, the results are rationalized in terms of a modified effective temperature model, whereby increasing the interface strength leads to higher effective temperatures and correspondingly to higher solubilities. Comparison of the segregation coefficient at the interphase boundary with solubilities in the bounding phases illustrates the effective temperature model is self-consistent and comprehensive. Past experimental studies on severe plastic deformation are found consistent with these observations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

In-Silico Design of Next Generation Cellulose-Derived Packaging Materials (CRADA Final Report)

Developing sustainable solutions for single-use packaging is an important objective to combat the environmental crisis of plastics pollution. Most embodiments of cellulose-based packaging materials, including CellophaneTM, are completely biodegradable in both terrestrial and marine environments. However, petroleum-derived alternatives offer some performance advantages for metrics such as moisture barriers and mechanical properties. This project leverages molecular dynamics simulation to investigate how molecular modifications to cellulose-based polymer assemblies impact their material properties. An important performance criterion for the modified materials was to retain biodegradability; thus, modifications by naturally occurring, biodegradable additives were the focus of this study. Specifically, we developed models with xylan and lignin of varying monomeric compositions into the cellulose matrix. The mechanical properties were investigated by performing stress-strain simulations, and the water barrier and hydrophobicity were investigated by simulating the water contact angle. Our findings indicate that the incorporation of xylan into the cellulose matrix tends to increase the mechanical properties with an optimal loading of ~27 wt%. We also predict that orienting the nanoscale directionality of the xylan chains such that they are perpendicular to the cellulose fibrils will dramatically increase mechanical strength. In contrast, the incorporation of lignin tends to weaken the composite at all loadings investigated. Simulations of water contact angle predicted that coating polymers on the surface of the cellulose assembly creates a more hydrophobic surface than incorporating them throughout the matrix. Of the coatings investigated, lignin resulted in the most hydrophobic surface, followed by pectin and keratin, which both imparted modest increases in hydrophobicity. Future experimental work done by Futamura will focus on designing material prototypes to capitalize on the predictions of performance enhancement obtained from molecular modeling. While substantial progress was made by the simulations performed in this project, there still exists a vast parameter space that we were unable to investigate, including branching, functional group decoration, and degree of polymerization of polymer additives. However, the methods developed in this initial investigation will facilitate more rapid evaluation of the impact of molecular characteristics on the performance of biopolymer composite materials and thereby accelerate future materials discovery efforts in this area.

36 MATERIALS SCIENCE↗