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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Atomistic modeling of energy band alignment in CdTe(1 0 0) and CdTe(1 1 1) surfaces

An atomic-scale perspective of energy band alignment in CdTe surfaces has not been spatially studied despite the major role surfaces play in forming interfaces within CdTe-based thin film photovoltaic devices. Atomistic modeling based on density functional theory coupled with surface Green’s function is used for calculating energy band alignment of CdTe surfaces. The CdTe(1 0 0) ((1×1) and c(2×2) reconstruction) and CdTe(1 1 1) ((1×1) and (2×2) reconstruction) facets without and with surface relaxation provide insightful band bending characteristics that influence charge carrier transport. Results show that unrelaxed (1×1) CdTe(1 0 0) and CdTe(1 1 1) surfaces bend the valence band downward with surface polarity dictating the surface potential magnitude. The reconstructed CdTe(1 0 0) c(2×2) and CdTe(1 1 1) (2×2) surfaces result in favorable surface electronic features in relation to their unreconstructed variants. In addition, the structurally relaxed CdTe(1 1 1) surfaces develop an internal energy cusp potential that may enhance hole charge transport toward the back of CdTe solar cell devices. In conclusion, energy band alignments calculated within the study lead to a detailed understanding of how CdTe surfaces may affect CdTe-based thin film photovoltaic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Atomistic modeling of metal–nonmetal interphase boundary diffusion

Atomistic computer simulations are applied to investigate the atomic structure, thermal stability, and diffusion processes in Al–Si interphase boundaries as a prototype of metal–ceramic interfaces in composite materials. Some of the most stable orientation relationships between the phases found in this work were previously observed in epitaxy experiments. Here, a non-equilibrium interface can transform to a more stable state by a mechanism that we call interface-induced recrystallization. Diffusion of both Al and Si atoms in stable Al–Si interfaces is surprisingly slow compared with diffusion of both elements in Al grain boundaries but can be accelerated in the presence of interface disconnections. A qualitative explanation of the sluggish interphase boundary diffusion is proposed. Atomic mechanisms of interphase boundary diffusion are similar to those in metallic grain boundaries and are dominated by correlated atomic rearrangements in the form of strings and rings of collectively moving atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2023

DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets

Graph neural networks (GNNs) are a class of Deep Learning models used in designing atomistic materials for effective screening of large chemical spaces. To ensure robust prediction, GNN models must be trained on large volumes of atomistic data on leadership class supercomputers. Even with the advent of modern architectures that consist of multiple storage layers that include node-local NVMe devices in addition to device memory for caching large datasets, extreme-scale model training faces I/O challenges at scale.We present DDStore, an in-memory distributed data store designed for GNN training on large-scale graph data. DDStore provides a hierarchical, distributed, data caching technique that combines data chunking, replication, low-latency random access, and high throughput communication. DDStore achieves near-linear scaling for training a GNN model using up to 1000 GPUs on the Summit and Perlmutter supercomputers, and reaches up to a 6.15x reduction in GNN training time compared to state-of-the-art methodologies.

Choi, Jong Youl↗
DOE OSTI2017

Evaluation of Alternative Atomistic Models for the Incipient Growth of ZnO by Atomic Layer Deposition

ZnO thin films are interesting for applications in several technological fields, including optoelectronics and renewable energies. Nanodevice applications require controlled synthesis of ZnO structures at nanometer scale, which can be achieved via atomic layer deposition (ALD). However, the mechanisms governing the initial stages of ALD had not been addressed until very recently. Investigations into the initial nucleation and growth as well as the atomic structure of the heterointerface are crucial to optimize the ALD process and understand the structure-property relationships for ZnO. We have used a complementary suite of in situ synchrotron x-ray techniques to investigate both the structural and chemical evolution during ZnO growth by ALD on two different substrates, i.e., SiO2 and Al2O3, which led us to formulate an atomistic model of the incipient growth of ZnO. The model relies on the formation of nanoscale islands of different size and aspect ratio and consequent disorder induced in the Zn neighbors' distribution. However, endorsement of our model requires testing and discussion of possible alternative models which could account for the experimental results. In this work, we review, test, and rule out several alternative models; the results confirm our view of the atomistic mechanisms at play, which influence the overall microstructure and resulting properties of the final thin film.

ALD↗
DOE OSTI2026

Conserved Macromolecular Architecture of Poplar Secondary Cell Walls Revealed by ssNMR and Atomistic Modeling

The macromolecular architecture of plant secondary cell walls governs wood's mechanical and biochemical properties, yet its natural intra-species variability remains poorly characterized. Here, we combined 13C solid-state NMR (ssNMR), multivariate statistical analysis, and molecular modeling to profile nanoscale structure across 13 genetically diverse Populus trichocarpa genotypes grown in 13C-enriched atmospheres. SsNMR-derived phenotypes spanning composition, structure, mobility, and inter-polymer proximities reveal a conserved architecture, with a subtle yet coordinated variation organizing into dominant structural and secondary mobility axes. A representative atomistic model captures these features and reproduces experimental metrics. Molecular dynamics simulations support a weak but consistent positive correlation between cellulose abundance and crystalline-like order, with interior cellulose chains enriched in tg (trans-gauche) conformations without expanding crystalline cores. Together, experiment and simulation reveal a genetically buffered, broadly conserved nanoscale architecture across genotypes, where subtle fine-tuning of cellulose bundling and matrix packing balances mechanical performance with biological function.

09 BIOMASS FUELS↗
DOE OSTI2026

Dataset_for_Conserved_macromolecular_architecture_of_Poplar_secondary_cell_walls_revealed_by_ssNMR_and_atomistic_modeling

This dataset contains solid-state 13C NMR data and atomistic molecular dynamics simulation files supporting the study of nanoscale secondary cell wall architecture across 13 genetically diverse Populus trichocarpa genotypes grown under uniform greenhouse conditions in 13C-enriched CO2 atmospheres (~89% 13C enrichment).The dataset contains two collections of solid-state 13C NMR data. (1) 200 MHz data (Bruker Avance III HD, 4 mm HX probe, 10 kHz MAS): raw Bruker TopSpin experiment folders and DMFIT-exported ascii spectra for selective and non-selective 1D 13C-13C spin diffusion experiments (3000 ms mixing) used to quantify inter-polymer spatial proximities, and short-mixing (1 ms) reference spectra used for polymeric abundance quantification by spectral deconvolution. (2) 600 MHz data (Bruker Avance III, 1.6 mm PhoenixNMR HXY probe, 30 kHz MAS): raw Bruker TopSpin experiment folders containing 2D CORD, 2D CP-INADEQUATE, and 13C/1H relaxation (T1, T1rho) experiments for all 13 genotypes, with processed Excel workbooks per experiment type. Molecular dynamics simulation code, coordinate files, and analysis scripts (NAMD/CHARMM/Python) for six atomistic cell wall models are included. Summarized ssNMR data are compiled into a single excel file and subjected to statistical analysis. Multivariate analysis code (PCA, Pearson correlation) and summary data are provided as excel worksheets and Jupyter notebooks (Python 3).

09 BIOMASS FUELS↗
DOE OSTI2023

Atomistic modeling of a superconductor–transition metal dichalcogenide–superconductor Josephson junction

Using an atomistic tight-binding model, we investigate the characteristics of a Josephson junction formed by monolayers of MoS 2 sandwiched between Pb superconducting electrodes. We derive and apply Green's function–based formulation to compute the Josephson current as well as the local density of states in the junction. Our analysis of diagonal and off-diagonal components of the local density of states reveals the presence of triplet superconducting correlations in the MoS 2 monolayers and spin-polarized subgap (Andreev bound) states. Our formulation can be extended to other systems where atomistic details and large scales are needed to obtain accurate modeling of Josephson junction physics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Emerging Atomistic Modeling Methods for Heterogeneous Electrocatalysis

Heterogeneous electrocatalysis lies at the center of various technologies that could help enable a sustainable future. However, its complexity makes it challenging to accurately and efficiently model at an atomic level. Herein, we review emerging atomistic methods to simulate the electrocatalytic interface with special attention devoted to the components/effects that have been challenging to model, such as solvation, electrolyte ions, electrode potential, reaction kinetics, and pH. Additionally, we review relevant computational spectroscopy methods. Then, we showcase several examples of applying these methods to understand and design catalysts relevant to green hydrogen. We also offer experimental views on how to bridge the gap between theory and experiments. Finally, we provide some perspectives on opportunities to advance the field.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Configurational-Bias Monte Carlo Back-Mapping Algorithm for Efficient and Rapid Conversion of Coarse-Grained Water Structures into Atomistic Models

Coarse-grained molecular dynamics (MD) simulations represent a powerful approach to simulate longer time scale and larger length scale phenomena than those accessible to all-atom models. The gain in efficiency, however, comes at the cost of atomistic details. The reverse transformation, also known as back-mapping, of coarse grained beads into their atomistic constituents represents a major challenge. Most existing approaches are limited to specific molecules or specific force-fields and often rely on running a long time atomistic MD of the back-mapped configuration to arrive at an optimal solution. Such approaches are problematic when dealing with systems with high diffusion barriers. Here, we introduce a new extension of the configurational-bias-Monte-Carlo (CBMC) algorithm, which we term the crystalline-configurational-bias-Monte-Carlo (C-CBMC) algortihm, that allows rapid and efficient conversion of a coarse-grained model back into its atomistic representation. Although the method is generic, we use a coarse-grained water model as a representative example and demonstrate the back-mapping or reverse transformation for model systems ranging from the ice-liquid water interface to amorphous and crystalline ice configurations. A series of simulations using the TIP4P/Ice model are performed to compare the new CBMC method to several other standard Monte Carlo and Molecular Dynamics based back-mapping techniques. In all the cases, the C-CBMC algorithm is able to find optimal hydrogen bonded configuration many thousand evaluations/steps sooner than the other methods compared within this paper. For crystalline ice structures such as a hexagonal, cubic, and cubic-hexagonal stacking disorder structures, the C-CBMC was able to find structures that were between 0.05 and 0.1 eV/water molecule lower in energy than the ground state energies predicted by the other methods. Detailed analysis of the atomistic structures show a significantly better global hydrogen positioning when contrasted with the existing simpler back-mapping methods. The error in the RDFs of backmapped relative to reference configuration for the CCBMC, MD, and MC were found to be 6.9, 8.7, and 12.9 respectively for the hexagonal system. For the cubic system the relative error of the RDFs for the CCBMC, MD, and MC were found to be 18.2, 34.6, and 39.0 respectively. Finally, our results demonstrate the efficiency and efficacy of our new back-mapping approach, especially for crystalline systems where simple force-field based relaxations have a tendency to get trapped in local minima.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2016

Phase transformations at interfaces: Observations from atomistic modeling

Here, we review the recent progress in theoretical understanding and atomistic computer simulations of phase transformations in materials interfaces, focusing on grain boundaries (GBs) in metallic systems. Recently developed simulation approaches enable the search and structural characterization of GB phases in single-component metals and binary alloys, calculation of thermodynamic properties of individual GB phases, and modeling of the effect of the GB phase transformations on GB kinetics. Atomistic simulations demonstrate that the GB transformations can be induced by varying the temperature, loading the GB with point defects, or varying the amount of solute segregation. The atomic-level understanding obtained from such simulations can provide input for further development of thermodynamics theories and continuous models of interface phase transformations while simultaneously serving as a testing ground for validation of theories and models. They can also help interpret and guide experimental work in this field.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Harness the power of atomistic modeling and deep learning in biofuel separation

Biofuels offer a remarkable, sustainable energy source for a future of clean energy. The development of efficient biofuel separation plays a crucial role in achieving cost-effective utilization of biofuel. In this chapter, we provide an overview of the recent advancements in atomistic-level modeling and deep learning in the rational design of novel, efficient biofuel separation. The fundamental principles of quantum and statistical mechanics are covered in appropriate detail to highlight their underlying differences in theory. The methodologies of several molecular representations and deep learning algorithms applicable to biofuel separation are briefly demonstrated as well. The applications, successes, and risks of employing density functional theory, ab initio molecular dynamics, classical molecular dynamics, and deep learning are provided to showcase their recent accomplishments in biofuel separation as well as potential improvements in both methodology and application. Lastly, a vision for the future growth of these methods is illustrated.

deep learning, artificial intelligence, biofuels, ↗
DOE OSTI2016

Atomistic modelling of scattering data in the Collaborative Computational Project for Small Angle Scattering (CCP-SAS)

The capabilities of current computer simulations provide a unique opportunity to model small-angle scattering (SAS) data at the atomistic level, and to include other structural constraints ranging from molecular and atomistic energetics to crystallography, electron microscopy and NMR. This extends the capabilities of solution scattering and provides deeper insights into the physics and chemistry of the systems studied. Realizing this potential, however, requires integrating the experimental data with a new generation of modelling software. To achieve this, the CCP-SAS collaboration (http://www.ccpsas.org/) is developing open-source, high-throughput and user-friendly software for the atomistic and coarse-grained molecular modelling of scattering data. Robust state-of-the-art molecular simulation engines and molecular dynamics and Monte Carlo force fields provide constraints to the solution structure inferred from the small-angle scattering data, which incorporates the known physical chemistry of the system. The implementation of this software suite involves a tiered approach in which GenApp provides the deployment infrastructure for running applications on both standard and high-performance computing hardware, and SASSIE provides a workflow framework into which modules can be plugged to prepare structures, carry out simulations, calculate theoretical scattering data and compare results with experimental data. GenApp produces the accessible web-based front end termed SASSIE-web , and GenApp and SASSIE also make community SAS codes available. Applications are illustrated by case studies: (i) inter-domain flexibility in two- to six-domain proteins as exemplified by HIV-1 Gag, MASP and ubiquitin; (ii) the hinge conformation in human IgG2 and IgA1 antibodies; (iii) the complex formed between a hexameric protein Hfq and mRNA; and (iv) synthetic `bottlebrush' polymers.

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

Molecular Understanding of Nitrogen Oxide Fixation of Water-Lean Carbon Capture Solvents by Atomistic Modeling

Nitrogen oxides, present in flue gas, can cause negative impacts on amine carbon capture solvents by the formation of heat-stable salts and suspected carcinogens. Thus, to maximize the performance of water-lean solvents, a better understanding of this process in these systems is necessary. Here, a computational study for the fixation of the CO 2 capture solvent N-(2-ethoxyethyl)-3-morpholinopropan-1-amine (EEMPA) to nitramine/nitrosamine was conducted. The first step involves the dissociation of the NH bond of EEMPA, in which the homolytic mechanism is energetically more favorable than the heterolytic mechanism. The second step involves radical recombination to form N–N bonds. While NO 2 directly reacts with EEMPA, NO has almost no effect. However, in the presence of O 2 , fixation of EEMPA by NO is enhanced via the formation of N 2 O 4 species. Finally, low reaction energies indicate that the formation of nitramine/nitrosamine may be a reversible process, suggesting that EEMPA could be recovered under thermal stripping conditions.

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

Atomistic modeling of tensile deformation and fracture of carbon fibers: Nanoscale stress redistribution, effect of local structural characteristics and nanovoids

The mechanisms of tensile deformation and fracture of carbon fibers are investigated in large-scale molecular dynamics simulations. The simulations performed for computational samples with different structural characteristics enable analysis of the structural sensitivity of the mechanical properties. It is found that the carbon fiber nanostructure can be represented by a fine (nanoscale) mixture of stiff and soft regions. Despite the nanoscale structural heterogeneity of the computational fibers, their fracture strain and tensile strength exceed the experimental values by almost an order of magnitude. Here, this observation is explained by the ability of the carbon fiber nanostructure, largely consisting of curved and folded layers of turbostratic carbon, to exhibit continuous nanoscale load redistribution during the deformation. The transient, fluid nature of a percolating cluster of stress-holding structural elements prevents stress localization at the early stage of the deformation and enables the fiber nanostructure to adapt to the increasing load. Overall, the results of the simulations suggest that the design of advanced carbon fiber manufacturing and post-processing methods focused on the elimination of critical mesoscopic structural defects (internal voids, foreign inclusions, and surface flaws) can yield a substantial increase in the strength and fracture strain of carbon fibers.

36 MATERIALS SCIENCE↗
DOE OSTI2022

An atomistic model for the thermal resistance of a liquid–solid interface

The thermal resistance associated with the interface between a solid and a liquid is analysed from an atomistic point of view. Partial evaluation of the associated Green–Kubo integral elucidates the various factors governing heat transport across the interface and leads to a quantitative model for the thermal resistance in terms of atomistic-level system parameters. The model is validated using molecular dynamics simulations.

Mechanics↗
DOE OSTI2018

Grain-Boundary Resistance in Copper Interconnects: From an Atomistic Model to a Neural Network

Orientation effects on the specific resistance of copper grain boundaries are studied systematically with two different atomistic tight-binding methods. A methodology is developed to model the specific resistance of grain boundaries in the ballistic limit using the embedded atom model, tight- binding methods, and nonequilibrium Green’s functions. The methodology is validated against first-principles calculations for thin films with a single coincident grain boundary, with 6.4% deviation in the specific resistance. A statistical ensemble of 600 large, random structures with grains is studied. For structures with three grains, it is found that the distribution of specific resistances is close to normal. Finally, a compact model for grain-boundary-specific resistance is constructed based on a neural network.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2018

Atomistic model of xenon gas bubble re-solution rate due to thermal spike in uranium oxide

Atomistic simulations are performed to study the response of Xe gas bubbles in UO 2 to ionizing fission products through the thermal spike approximation. A portion of the total electronic stopping power (S e ) is taken as the thermal spike energy through a ratio variable ζ. The thermal spike energy causes extreme melting within the fission track cylindrical region. Molecular dynamics is employed to quantify the probability of a Xe gas atom to be re-solved (re-dissolved) back into the UO 2 matrix. Subsequently, a re-solution model is developed and parametrized as a function of bubble radius (R), off-centered distance (r), and thermal spike energy (ζS e ). The off-centered distance measures the shift of the thermal spike axis from the bubble center. To evaluate the re-solution model, independent fission product yield of U-235 fission due to thermal neutrons (0.0253 eV), taken from the JEFF-3.3 database, is used. The kinetic energy of the fission products is taken from the EXFOR database. Subsequently, the decay of Se over distance for each fission product is simulated. Finally, the evaluated re-solution rate (re-solution probability per second) is presented as a function of bubble radius for a range of ζ.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

Reshaping and sintering of 3D fcc metal nanoclusters: Stochastic atomistic modeling with realistic surface diffusion kinetics

Far-from-equilibrium evolution of metallic nanocluster shapes is highly sensitive to the atomistic-level details of surface diffusion for diverse local surface configurations. A stochastic model was developed incorporating realistic values for the multiple diffusion barriers (contrasting previous unrealistic generic prescriptions) based upon insights from homoepitaxial film growth. Kinetic Monte Carlo simulation then elucidates the conversion of Ag nanocubes to Wulff polyhedra mediated by nucleation of new {100} facets, the pinch-off of sufficiently elongated Ag nanorods, and key aspects of sintering for orientationally aligned Ag and Au nanoclusters. In conclusion, the time scale for sintering of Au nanoclusters observed in high-resolution transmission electron microscopy studies was also recovered.

36 MATERIALS SCIENCE↗