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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Multiscale affinity maturation simulations to elicit broadly neutralizing antibodies against HIV

The design of vaccines against highly mutable pathogens, such as HIV and influenza, requires a detailed understanding of how the adaptive immune system responds to encountering multiple variant antigens (Ags). Here, we describe a multiscale model of B cell receptor (BCR) affinity maturation that employs actual BCR nucleotide sequences and treats BCR/Ag interactions in atomistic detail. We apply the model to simulate the maturation of a broadly neutralizing Ab (bnAb) against HIV. Starting from a germline precursor sequence of the VRC01 anti-HIV Ab, we simulate BCR evolution in response to different vaccination protocols and different Ags, which were previously designed by us. The simulation results provide qualitative guidelines for future vaccine design and reveal unique insights into bnAb evolution against the CD4 binding site of HIV. Our model makes possible direct comparisons of simulated BCR populations with results of deep sequencing data, which will be explored in future applications.

Conti, Simone (ORCID:000000017986567X)↗
DOE OSTI2025

Molecular Dynamics Simulations of Supercritical Carbon Dioxide and Water using TraPPE and SWM4-NDP Force Fields

The increased levels of carbon dioxide (CO 2 ) emissions due to the combustion of fossil fuels and the consequential impact on global climate change have made CO 2 capture, storage, and utilization a significant area of focus for current research. In most electrochemical CO 2 applications, water is used as a proton donor due to its high availability and mobility and use as a polar solvent. Additionally, supercritical CO 2 is a promising avenue for electrochemical applications due to its unique chemical and physical properties. Consequently, understanding the interactions between water and supercritical CO 2 is of great importance for future electrochemical applications. Molecular dynamics (MD) simulation is a powerful tool that enables atomistic-resolution dynamics of molecular systems, which can complement and guide future experimental investigations. This study employed atomistic MD to study the cosolubilities, codiffusivities, and structure of supercritical CO 2 and water systems, with a polarizable water model (SWM4-NDP) and a nonpolarizable CO 2 model (TraPPE). Additionally, ab initio MD simulations were used to better understand how atomistic polarizable/nonpolarizable models compare to explicit modeling of electron densities. The polarizable water model exhibited substantial improvement in water-associated properties. In conclusion, we anticipate the development of a compatible polarizable CO 2 model to yield similar improvement, providing a pathway for realizing novel high-pressure electrochemical systems.

25 ENERGY STORAGE↗
DOE OSTI2025

HydraGNN v4.0

The new version of HydraGNN v4.0 provides additional core capabilities, such as: Inclusion of multi-body atomistic cluster expansion MACE, polarizable atom interaction neural network PAINN, and equivariant principal neighborhood aggregation (PNAEq) among the message passing layers supported -Inclusion of graph transformers to directly model long-range interactions between nodes that are distant in the graph topology Integration of graph transformers with message passing layers by combining the graph embedding generated by the two mechanisms, which allows for an improved expressivity of the HydraGNN architecture Improved re-implementation of multi-task learning (MTL) to allow its use for stabilized training across imbalanced, multi-source, multi-fidelity data Introduction of multi-task parallelism, a newly proposed type of model parallelism specifically for MTL architectures, which allows to dispatch different output decoding heads to different GPU devices Integration of multi-task parallelism with pre-existing distributed data parallelism to enable a 2D parallelization for distributed training Improved portability of the distributed training across Intel GPUs, which has been testes on ALCF exascale supercomputer Aurora Inclusion of 2-level fine-grained energy profilers portable across NVIDIA, AMD, and Intel GPUs to monitor the power and energy consumption associated with different functions executed by the HydraGNN code during data pre-load and training Restructuring of previous examples and inclusion of new sets of examples to illustrate the download, preprocess, and training of HydraGNN models on new large-scale open-source datasets for atomistic materials modeling (e.g., Alexandria, Transition1x, OMat24, OMol25)

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗
DOE OSTI2025

Universal Electronic‐Structure Relationship Governing Intrinsic Magnetic Properties in Permanent Magnets

An electronic-structure-centered perspective is presented on permanent-magnet (PM) design, highlighting two key levers, that is, saturation magnetization (M s ), governed by 3d-band filling and exchange physics, and magnetocrystalline anisotropy energy (MAE), arising from spin-orbit coupling (SOC) on anisotropic orbital populations. Reviewing current practices, including DFT-based MAE/J ij extraction, atomistic-spin and micromagnetic modeling, and high-throughput machine learning (ML) pipelines, three bottlenecks limiting predictive discovery is identified that is i) electronic-structure accuracy for small MAE (sensitive to functional choice, Hubbard U, and many-body effects), ii) finite-temperature and kinetic realism (phonon/magnon renormalization, ordering kinetics), and iii) descriptor and multiscale decoupling (lack of SOC-weighted and orbital-resolved fingerprints). Deep dives into the electronic-structure of Nd─Fe─B and Fe─N show how these fingerprints govern magnetic performance, motivating DFT- and quantum-mechanics-based descriptors for discovery. Unbiased, structure-driven exploration, coupled with high-throughput simulations, ML, generative AI, and reasoning models, accelerates candidate identification and propagates insights across scales. Addressing supply-chain risks, on future needs of designing “critical-element-free” magnets with tailored microstructure and high energy products is emphasized. By integrating electronic fingerprints, AI reasoning, and multiscale modeling, a practical roadmap is provided for rare-earth-lean or rare-earth free, high-performance, sustainable PMs.

Singh, Prashant [Ames Laboratory, and Iowa State U↗
DOE OSTI2023

Building a DFT+U machine learning interatomic potential for uranium dioxide

Despite uranium dioxide (UO 2 ) being a widely used nuclear fuel, fuel performance models rely extensively on empirical correlations of material behavior, leveraging the historical operating experience of UO 2 . Mechanistic models that consider an atomistic understanding of the processes governing fuel performance (such as fission gas release and creep) will enable a better description of fuel behavior under non-prototypical conditions such as in new reactor concepts or for modified UO 2 fuel compositions. To this end, molecular dynamics simulation is a powerful tool for rapidly predicting physical properties of proposed fuel candidates. However, the reliability of these simulations depends largely on the accuracy of the atomic forces. Traditionally, these forces are computed using either a classical force field (FF) or density functional theory (DFT). While DFT is relatively accurate, the computational cost is burdensome, especially for f-electron elements, such as actinides. By contrast, classical FFs are computationally efficient but are less accurate. For these reasons, we report a new accurate machine learning interatomic potential (MLIP) for UO 2 that provides high-fidelity reproduction of DFT forces at a similar low cost to classical FFs. We employ an active learning approach that autonomously augments the DFT training data set to iteratively refine the MLIP. To further improve the quality of our predictions, we utilize transfer learning to retrain our MLIP to higher-accuracy DFT+U data. We validate our MLIPs by comparing predicted physical properties (e.g., thermal expansion and elastic properties) with those from existing classical FFs and DFT/DFT+U calculations, as well as with experimental data when available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Role of Oxidizing Conditions in the Dispersion of Supported Platinum Nanoparticles Explored by Ab Initio Modeling

Achieving fine control over the dispersion of supported platinum nanoparticles (Pt) is a promising avenue to enhancing their catalytic activity and selectivity. Experimental observations suggest that exposing ceria-supported Pt nanoparticles to O 2 at 500 °C promotes their dispersion into smaller particles and eventually single atoms. The exact role of oxygen in this process is not yet well understood. Past density functional theory studies of ceria-supported Pt have typically narrowed their scope to single atoms and clusters of a few atoms. Herein, we combine several approaches and types of models in a consistent atomistic framework to evaluate the relative stability of ceria-supported Pt as a function of the degree of oxidation of Pt and of the particle size, ranging from single atoms to nanoparticles 1.5 nm of diameter. Finally, we find that the largest nanoparticles remain the thermodynamically most stable species on the lowest energy facet of ceria even under oxidizing conditions, suggesting that stronger adsorption sites are required to stabilize smaller clusters and single atoms and promote oxidative dispersion.

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

Ligand-Induced Size-Dependent Circular Dichroism in Quantum Dots

Recent experiments have probed the chiral properties of semiconductor nanocrystal (NC) quantum dots (QDs), but understanding the circular dichroism line shape, excitonic features, and chirality induction mechanism remains a challenge. We propose an atomistic pseudopotential method to model chiral ligand passivated QDs, computing circular dichroism (CD) spectra for CdSe QDs (2.6-3.8 nm). We find strong agreement between calculated and measured line shapes, predicting consistent bisignate line shapes with decreasing CD magnitude as size increases. Further, our analysis reveals the origin of bisignate line shapes, arising from nondegenerate excitons with opposing angular momenta. We also explore the impact of chiral ligand orientation on QD surfaces, observing changes in the optical activity magnitude and sign. This orientation sensitivity offers the means to distinguish ordered from disordered ligand configurations, facilitating the study of order-disorder transitions at ligand-QD interfaces.

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

Stress-Dependent Chemo-Mechanical Performance of Amorphous Si Anodes for Li-Ion Batteries upon Lithiation

Alloying-type anodes are significantly governed by their chemo-mechanical performance during the electrochemical cycling. The reaction-induced huge volumetric change of these anodes may cause material degradation and failure under mechanical constraints. Here, we investigate the stress-dependent lithiation behavior of amorphous Si (a-Si) anodes using molecular dynamics simulations. It is indicated that a-Si anodes can sustain higher hydrostatic stress than biaxial/uniaxial ones without the occurrence of mechanical failure. Thermodynamic and electrochemical calculations demonstrate that although the lithiation procedure also affects the thermodynamic stability of a-Si anodes, it is mainly dominated by the external mean stresses. Compressive stress is confirmed to destabilize a-Si anodes and further trigger their capacity fading. Compared with our atomistic simulations, previous continuum models underestimate the open-cell potentials of a-Si anodes, due to their ignored large volumetric deformation at higher stresses and Li concentrations. Finally, this computational study provides the intensive atomic-level understanding of the stress-dependent lithiation behavior of a-Si anodes.

25 ENERGY STORAGE↗
DOE OSTI2023

Energy localization efficiency in 1,3,5-trinitro-2,4,6-triaminobenzene pore collapse mechanisms

Atomistic and continuum scale modeling efforts have shown that the shock-induced collapse of porosity can occur via a wide range of mechanisms dependent on pore morphology, the shockwave pressure, and material properties. The mechanisms that occur under weaker shocks tend to be more efficient at localizing thermal energy but do not result in high, absolute temperatures or spatially large localizations compared to mechanisms found under strong shock conditions. However, the energetic material 1,3,5-trinitro-2,4,6-triaminobenzene (TATB) undergoes a wide range of collapse mechanisms that are not typical of similar materials, leaving the collapse mechanisms and the resultant energy localization from the collapse, i.e., hotspots, relatively uncharacterized. Therefore, we present the pore collapse simulations of cylindrical pores in TATB for a wide range of pore sizes and shock strengths that trigger viscoplastic collapses that occur almost entirely perpendicular to the shock direction for weak shocks and hydrodynamic-like collapses for strong shocks that do not break the strong hydrogen bonds of the TATB basal planes. The resulting hotspot temperature fields from these mechanisms follow trends that differ considerably from other energetic materials; hence, we compare them under normalized temperature values to assess the relative efficiency of each mechanism to localize energy. The local intra-molecular strain energy of the hotspots is also assessed to better understand the physical mechanisms behind the phenomena that lead to a latent potential energy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Transferring predictions of formation energy across lattices of increasing size*

In this study, we show the transferability of graph convolutional neural network (GCNN) predictions of the formation energy of the nickel-platinum solid solution alloy across atomic structures of increasing sizes. The original dataset was generated with the large-scale atomic/molecular massively parallel simulator using the second nearest-neighbor modified embedded-atom method empirical interatomic potential. Geometry optimization was performed on the initially randomly generated face centered cubic crystal structures and the formation energy has been calculated at each step of the geometry optimization, with configurations spanning the whole compositional range. Using data from various steps of the geometry optimization, we first trained our open-source, scalable implementation of GCNN called HydraGNN on a lattice of 256 atoms, which accounts well for the short-range interactions. Using this data, we predicted the formation energy for lattices of 864 atoms and 2048 atoms, which resulted in lower-than-expected accuracy due to the long-range interactions present in these larger lattices. We accounted for the long-range interactions by including a small amount of training data representative for those two larger sizes, whereupon the predictions of HydraGNN scaled linearly with the size of the lattice. Therefore, our strategy ensured scalability while reducing significantly the computational cost of training on larger lattice sizes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Dislocation loop evolution in Kr-irradiated ThO 2

Here, the early stage of microstructural evolution of ThO 2 , under krypton irradiation at 600, 800, and 1000°C, was investigated using in situ transmission electron microscopy (TEM). Dislocation loops grew faster, whereas their number density decreased with increasing irradiation temperature. Loop density was found to decrease with ion dose. Interstitial dislocation loops, including Frank loops with Burgers vector of $\textit{a}/3\langle111\rangle$ and perfect loops with Burgers vector of $\textit{a}/2\langle110\rangle$, were determined by traditional TEM and atomic resolution–scanning TEM techniques. Atomistic and mesoscale level modeling are performed to interpret experimental observations. The migration energy barriers of defects in ThO 2 were calculated using density-functional theory. The energetics of different dislocation loop types were studied using molecular dynamics simulations. Loop density and diameter were analyzed using a kinetic rate theory model that considers stoichiometric loop evolution. This analysis reveals that loop growth is governed by the mobility of cation interstitials, whereas loop nucleation is determined by the mobility of anion defects. Lastly, a rate theory model was used to extract the diffusion coefficients of thorium interstitials, oxygen interstitials, and vacancies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Understanding and Strategies for Controlled Interfacial Phenomena in Lithium-Ion Batteries and Beyond

Electrolyte chemistry and properties and electrode structure and chemical properties particularly at the interfaces are crucial for the development of advanced battery components. This project focused on elucidating the roles of the electrolyte and electrode on the formation and evolution of the SEI layer and cell electrochemical performance on silicon and on Li metal anodes. For Si nano/microstructures we evaluated lithiation, volume changes, reactivity, and chemo-mechanical transformations as functions of nanoparticle size, shape, presence of coatings, electrolyte composition, electron leakage to the electrolyte, and cycling. For Li metal anodes we provided detailed analyses of the electrode and electrolyte effects on interfacial reactivity and as a function of microstructural evolution and underlying stochasticity as dendrites nucleate and grow. We thoroughly investigated and simulated how the chemistry of the various components of the electrolyte and the electrode architecture may affect the electrochemical reactions as well as cell degradation. A multiscale modeling approach was utilized, where atomistic simulations informed about the microscopic behavior of the system, and intermediate time and length scales were investigated with mesoscopic models. Results were also tested against selected experiments from our collaborators. The combined theoretical-experimental strategy saves costs by utilizing predictions from computational analyses to guide experimentation.

25 ENERGY STORAGE↗
NASA NTRS2005

Effect of electron-nuclear spin interactions for electron-spin qubits localized in InGaAs self-assembled quantum dots

The effect of electron-nuclear spin interactions on qubit operations is investigated for a qubit represented by the spin of an electron localized in an InGaAs self-assembled quantum dot. The localized electron wave function is evaluated within the atomistic tight-binding model. The electron Zeeman splitting induced by the electron-nuclear spin interaction is estimated in the presence of an inhomogeneous environment characterized by a random nuclear spin configuration, by the dot-size distribution, alloy disorder, and interface disorder. Due to these inhomogeneities, the electron Zeeman splitting varies from one qubit to another by the order of 10(-6), 10(-6), 10(-7), and 10(-9) eV, respectively. Such fluctuations cause errors in exchange operations due to the inequality of the Zeeman splitting between two qubits. However, the error can be made lower than the quantum error threshold if an exchange energy larger than 10(-4) eV is used for the operation. This result shows that the electron-nuclear spin interaction does not hinder quantum-dot based quantum computer architectures from being scalable even in the presence of inhomogeneous environments.

Whaley, K. Birgitta↗
NASA NTRSDate not supplied

Computationally Guided Design of Polymer-Coated Microparticles as Reusable Materials

Long-duration space exploration missions and sustained lunar or Martian surface operations present greater demands for multifunctional and reusable materials. By scaling down the amount of material to be launched from Earth, both mission cost and risk can be reduced. In this regard, leveraging in-space manufacturing capabilities with reusable feedstock materials is an attractive option, as it will allow for articles to be generated on demand, utilized, and then recycled for additional use. NASA’s Enabling Sustained Presence Using Recyclables (ESPUR) project aims to develop reusable materials using polymer-coated microparticles that are bonded via reversible Diels-Alder reactions, where only modest heat is needed to trigger the reverse reaction and enable reuse. For proof-of-concept demonstration, research is currently focused on the fabrication of epoxy microparticles that contain a copoly(carbonate urethane) coating with maleimide and furan functionalities. Here, we discuss the integration of computational materials modeling approaches to help navigate the large design space in this development effort. We perform molecular dynamics (MD) simulations with atomistic and coarse-grained models of the copolymer, which allow us to evaluate the effects of design parameters like the molecular weight and composition on the molecular interactions and chain dynamics. We show how the properties change with the reversible bonds. We also leverage discrete element method (DEM) simulations to assess how microparticle design parameters like the size ratio and volume fraction can be tuned to increase the packing density and number of microparticle contacts to improve the mechanical properties. Our results demonstrate how computational tools can be used in close collaboration with experimental efforts to accelerate material design.

reusable materials↗
DOE OSTI2022

Development of a Laser Ultrasonics-based Approach for Rapid Screening of High Entropy Alloys

This project utilized a laser ultrasonic technique to systematically study temperature-induced evolution of material properties in a set of interrelated binary alloys and a high entropy alloy (HEA) fabricated using arc-melting and spark plasma sintering processes. This technique involved the use of a nanosecond duration, high-intensity pulsed laser to thermo-elastically generate ultrasonic waves that propagate in the bulk of the metal alloy. Sub-nanometer-scale displacements associated with the propagating bulk ultrasonic waves were detected along the epicenter on the opposite surface of the sample using a 1 GHz bandwidth photorefractive interferometer. Phase transformations and microstructural changes were inferred from the temperature-dependent trends in the bulk acoustic velocities and features in the ultrasonic epicentral waveforms measured in the binary alloys. These inferences were then correlated with electron/optical microscopy observations and predictions using calculations of phase diagrams (CALPHAD). The results showed that the laser-generated ultrasonic pulses were strongly influenced by changes in material microstructure and could accurately track thermally driven phase transformations and detect the presence of microscale heterogeneities (grain boundaries, dendritic structures, etc.) in the set of binary alloy samples. The measurement approach was then applied to a quinary HEA sample for estimating phase transition temperature and determining microstructural heterogeneity. The rapid, non-contact and non-destructive ultrasonic testing approach demonstrated here is amenable to high throughput combinatorial investigations that can be applied to graded composition HEAs produced using advanced manufacturing methods. When paired with atomistic simulations and CALPHAD modeling, this approach can overcome the bottlenecks faced by current material characterization methods in efficiently screening the vast discovery space of HEAs that spans over a hundred million unique quinary alloy compositions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Density functional theory-based surrogate kinetic models for heterogeneous reactions of hydrocarbon intermediates on silicon carbide

The increasing demand for high-performance materials in advanced technologies highlights the importance of achieving a fundamental understanding and potential control of silicon carbide (SiC) deposition processes. However, existing models often lack sufficient theoretical detail, relying heavily on empirical data and offering limited predictive capability. In particular, the complex surface chemistry governing SiC growth remains poorly understood. This study addresses these challenges by employing density functional theory (DFT) to investigate key heterogeneous reactions involving hydrocarbon intermediates on SiC surfaces, including dehydrogenation, hydrogenation, and carbon deposition. Transition state searches were conducted to identify reaction pathways and energy barriers. While first-principles calculations offer high accuracy, they are computationally intensive. To extend the utility of these first-principles results, vibrational analyses were performed using phonon-based statistical thermochemistry to compute temperature-dependent reaction rates which were used to develop Arrhenius-type surrogate kinetic models. Furthermore, the resulting framework provides a more rigorous, physically grounded basis for integrating atomistic insights into continuum-scale modeling, ultimately enabling improved prediction and optimization of SiC film growth in high-performance material systems.

Density Functional Theory↗
DOE OSTI2023

Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps

Abstract Interpretation of cryo-electron microscopy (cryo-EM) maps requires building and fitting 3D atomic models of biological molecules. AlphaFold-predicted models generate initial 3D coordinates; however, model inaccuracy and conformational heterogeneity often necessitate labor-intensive manual model building and fitting into cryo-EM maps. In this work, we designed a protein model-building workflow, which combines a deep-learning cryo-EM map feature enhancement tool, CryoFEM (Cryo-EM Feature Enhancement Model) and AlphaFold. A benchmark test using 36 cryo-EM maps shows that CryoFEM achieves state-of-the-art performance in optimizing the Fourier Shell Correlations between the maps and the ground truth models. Furthermore, in a subset of 17 datasets where the initial AlphaFold predictions are less accurate, the workflow significantly improves their model accuracy. Our work demonstrates that the integration of modern deep learning image enhancement and AlphaFold may lead to automated model building and fitting for the atomistic interpretation of cryo-EM maps.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Uniting Theory and Experiment to Deliver Flexible MOFS for Superior Methane (NG) Storage

The objective of the project was to use previous insights developed through synthesis and quantitative modeling of rigid metal–organic frameworks (MOFs) in an established synergistic theoretical/experimental team to create, modify, and evaluate flexible MOFs (FlexMOFs) for natural gas (NG) storage and release at practically useful pressures and transform the NG storage economy. The goal was reduced pressure absorbed natural gas (ANG) FlexMOF storage at operating pressures less than 100 bar with physisorption exploiting the favorable thermodynamics and kinetics of flexible porous material opening in response to adsorption. Hydrogen behavior in this context was also considered. The specific aim of the project was to design and develop a standard computational modeling methodology for, first, detailed atomistic retrodiction of FlexMOF gating behavior and ultimately prediction of the effects of functionalization and/or substitution on structural transitioning. The project was also geared towards the establishment of the interaction of methane with the framework and binding sites using modeling and the FlexMOF will be both internally and external validated as a SMART metric. This has value to the scientific community both from the obvious standpoint of providing a better understanding of the promising test case systems (CdIF-13 and the MIL-53(Al) series of MOFs), but also in providing an avenue of obtaining insight into these FlexMOF systems in general, which is of particular interest given the tendency of structure-function correlation to lag behind synthesis methodology (making the latter a hit or miss proposition for applications). This gap remains significant for FlexMOF systems whose gate opening behaviors complicate computational examination. The computational methodologies are relatively inexpensive in terms of both money and computational resources enhancing the general viability of these methodologies. The ultimate gain to the public will be in the application of these techniques to design systems for natural gas storage and use to cut down on green-house emissions.

03 NATURAL GAS↗