Acid–Base Properties of Cis-Vacant Montmorillonite Edge Surfaces: A Combined First-Principles Molecular Dynamics and Surface Complexation Modeling Approach
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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
SEARCH · Engineering Papers
Results for “molecular modelling”
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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Data from "Deep Potential Molecular Dynamics Simulations of Low-Temperature Plasma-Surface Interactions"
Data and input files related to the paper "Deep Potential Molecular Dynamics Simulations of Low-Temperature Plasma-Surface Interactions" (https://doi.org/10.1116/6.0004027). This includes the final DP model used in all simulations, training data set, example input files to run DeepMD (with LAMMPS), and data tables summarizing the results obtained from the simulations.
machine learning models↗
Comparison of Spacecraft Contamination Models with Well-Defined Flight Experiment
The report presents analyzed surface areas on particular experiment trays from the Long Duration Exposure Facility (LDEF) for silicone-based molecular contamination. The trays for examination were part of the Ultra-Heavy Cosmic Ray Experiment (UHCRE). These particular trays were chosen because each tray was identical to the others in construction, and the materials on each tray were well known, documented, and characterized. In particular, a known specific source of silicone contamination was present on each tray. Only the exposure conditions varied from tray to tray. The results of post-flight analyses of surfaces of three trays were compared with the predictions of the three different spacecraft molecular contamination models. Phase one tasks included: 1) documenting the detailed geometry of the hardware; 2) determining essential properties of the anodized aluminum, Velcro(Tm), silverized Teflon(Tm), silicone gaskets, and DC6-1104(Tm) silicone adhesive materials used to make the trays, tray covers, and thermal control blankets; 3) selecting and removing areas from each tray; and 4) beginning surface analysis of the selected tray walls. Phase two tasks included: 1) completion of surface analysis measurements of the selected tray surface, 2) obtaining auger depth profiles at selected locations, and 3) running versions of the ISEM, MOFLUX, and PLIMP (Plume Impingement) contamination prediction models and making comparisons with experimental results.
Pippin, G. H.↗
Investigation of the High-Energy Oxidation of FiberForm from DSMC Analysis of Molecular Beam Experiments
A collaborative effort between the University of Illinois at Urbana-Champaign (UIUC), NASA Ames Research Center (ARC) and Montana State University (MSU) succeeded at developing a new finite-rate carbon oxidation model from molecular beam scattering experiments on vitreous carbon (VC). We now aim to use the direct simulation Monte Carlo (DSMC) code SPARTA to apply the model to each fiber of the porous fibrous Thermal Protection Systems (TPS) material FiberForm (FF). The detailed micro-structure of FF was obtained from X-ray micro-tomography and then used in DSMC. Both experiments and simulations show that the CO/O products ratio increased at all temperatures from VC to FF. We postulate this is due to the larger number of collisions an O atom encounters inside the porous FF material compared to the flat surface of VC. For the simulations, we particularly focused on the lowest and highest temperatures studied experimentally, 1023 K and 1823 K, and found good agreement between the finite-rate DSMC simulations and experiments.
Oxidation↗
An idealized model for collapse of a molecular core within a filament
Here, we present an analytic treatment of the self-similar collapse of a uniform density ellipsoid to linear order in the deviations from sphericity. First we obtain a self-consistent closed-form solution for the evolution of an isolated ellipsoid and then impose the effects of an external forcing. This model describes the evolution under gravity of a pre-stellar core of molecular gas embedded in a much larger and lower-density filament. We compare with numerical solutions for the collapse without the limitation of small deviations. These show how the external perturbing force producing the initial deviation from sphericity is eventually surpassed by the anisotropic forces generated by the collapsing ellipsoid itself. This model should be useful in interpreting the way in which environment shapes the evolution of pre-stellar cores.
79 ASTRONOMY AND ASTROPHYSICS↗
Bridging the length scales in ionic separations via data-driving machine learning
We pursued a data science driven machine learning (ML) approach that blended molecular scale attributes informed from molecular dynamics (MD) simulation and materials properties to the selectivity and energy efficiency in targeted ionic separations using electric fields. The model mixtures investigated for ionic separations are pH sensitive and include organic acids, silica and boron, transition metals, such as copper and chromium. There were two major research thrusts of this project. Firstly, we investigated surrogate models and deep learning that relate material chemistries and structures to selective transport of ionic species under applied electric fields. Secondly we investigated how the bipolar junction interfacial design and water dissociation catalyst in bipolar membranes affect reverse bias polarization behavior and pH modulation in deionization platforms as a function of the platform operating parameters (e.g., cell voltage, residence time, and salt feed concentration). As a result of this work, we also were able to start a new direction, namely ML models for molecular design of surfactants.
36 MATERIALS SCIENCE↗
Thermal conductivity of high polymers.
Thermal conductivity theory based on molecular structure model to study linear amorphous high polymers, specifically polyethylene
MOLECULAR STRUCTURE↗
An integrated computational materials engineering framework to analyze the failure behaviors of carbon fiber reinforced polymer composites for lightweight vehicle applications
A bottom-up multi-scale modeling approach is used to develop an Integrated Computational Materials Engineering (ICME) framework for carbon fiber reinforced polymer (CFRP) composites, which has the potential to reduce development to deployment lead time for structural applications in lightweight vehicles. In this work, we develop and integrate computational models comprising of four size scales to fully describe and characterize three types of CFRP composites. In detail, the properties of the interphase region are determined by an analytical gradient model and molecular dynamics analysis at the nano-scale, which is then incorporated into micro-scale unidirectional (UD) representative volume element (RVE) models to characterize the failure strengths and envelopes of UD CFRP composites. Then, the results are leveraged to propose an elasto-plastic-damage constitutive law for UD composites to study the fiber tows of woven composites as well as the chips of sheet molding compound (SMC) composites. Subsequently, the failure mechanisms and failure strengths of woven and SMC composites are predicted by the meso-scale RVE models. Finally, building upon the models and results from lower scales, we show that a homogenized macro-scale model can capture the mechanical performance of a hat-section-shaped part under four-point bending. Along with the model integration, we will also demonstrate that the computational results are in good agreement with experiments conducted at different scales. The present work illustrates the potential and significance of integrated multi-scale computational modeling tools that can virtually evaluate the performance of CFRP composites and provide design guidance for CFRP composites used in structural applications.
36 MATERIALS SCIENCE↗
Space station neutral external environment
Molecular contamination levels arising from the external induced neutral environment of the Space Station (Phase 1 configuration) were calculated using the MOLFLUX model. Predicted molecular column densities and deposition rates generally meet the Space Station contamination requirements. In the doubtful cases of deposition due to materials outgassing, proper material selection, generally excluding organic products exposed to the external environment, must be considered to meet contamination requirements. It is important that the Space Station configuration, once defined, is not significantly modified to avoid introducing new unacceptable contamination sources.
Ehlers, H.↗
Size-Transferable Prediction of Excited State Properties for Molecular Assemblies with a Machine Learning Exciton Model
Computational modeling of the excited states of molecular aggregates faces significant computational challenges and size heterogeneity. Current machine learning (ML) models, typically trained on specific-sized aggregates, struggle with scalability. We found that the exciton model Hamiltonian of large aggregates can be decomposed into dimer pairs, allowing an ML model trained on dimers to reconstruct Hamiltonians for aggregates of any size. We also proposed a new method to address the phase-correction problem by introducing coupling terms’ approximations. Our model accurately predicted the excitation energies of the trimer and tetramer of perylene and tetracene and estimated S1 oscillator strengths of perylene aggregates. Leveraging our ML model, the optical gaps of nanosized perylene aggregates with up to 50 monomers are analyzed, qualitatively revealing the role of different couplings on their size dependency. Future work will explore transferability across different monomers to predict optical properties in heterogeneous assemblies.
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Electronic coarse-graining of long conjugated molecules: Case study of non-fullerene acceptors
By considering only one electronic state per molecule, charge transport models of molecular solids neglect intramolecular charge transfer. This approximation excludes materials with quasi-degenerate spatially separated frontier orbitals, such as non-fullerene acceptors (NFAs) and symmetric thermally activated delayed fluorescence emitters. By analyzing the electronic structure of room-temperature molecular conformers of a prototypical NFA, ITIC-4F, we conclude that the electron is localized on one of the two acceptor blocks with the mean intramolecular transfer integral of 120 meV, which is comparable with intermolecular couplings. Therefore, the minimal basis for acceptor–donor–acceptor (A–D–A) molecules consists of two molecular orbitals localized on the acceptor blocks. This basis is robust even with respect to geometry distortions in an amorphous solid, in contrast to the basis of two lowest unoccupied canonical molecular orbitals withstanding only thermal fluctuations in a crystal. The charge carrier mobility can be underestimated by a factor of two when using single site approximation for A–D–A molecules in their typical crystalline packings.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Enhanced deep potential model for fast and accurate molecular dynamics: application to the hydrated electron
Enhancements to the Deep Potential network integrate message-passing, achieving high accuracy with significant speed advantages. Applied to solvated electron in bulk water, confirming the cavity model and determining structural properties.
Chemistry↗
Influence of temporal variations in plasma conditions on the electric potential near self-organized dust chains
Self-organization of dust grains into stable filamentary dust structures (or “chains”) largely depends on dynamic interactions between individual charged dust grains and complex electric potential arising from the distribution of charges within a local plasma environment. Recent studies have shown that the positive column of the gas discharge plasma in the Plasmakristall-4 (PK-4) experiment at the International Space Station supports the presence of fast-moving ionization waves, which lead to variations of plasma parameters by up to an order of magnitude from the average background values. The highly variable environment resulting from ionization waves may have interesting implications for the dynamics and self-organization of dust particles, particularly concerning the formation and stability of dust chains. Here, we investigate the electric potential surrounding dust chains in the PK-4 experiment by employing a molecular dynamics model of the dust and ions with boundary conditions supplied by a particle-in-cell with Monte Carlo collision simulation of the ionization waves. The model is used to examine the effects of the plasma conditions within different regions of the ionization wave and compare the resulting dust structure to that obtained by employing the time-averaged plasma conditions. Here, the comparison between simulated dust chains and experimental data from the PK-4 experiment shows that the time-averaged plasma conditions do not accurately reproduce observed results for dust behavior, indicating that more careful treatment of plasma conditions in the presence of ionization waves is required. It is further shown that commonly used analytic forms of the electric potential do not accurately describe the electric potential near charged dust grains under these plasma conditions.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
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↗
Detection of organic vapors by polymer-composite coated quartz crystal microbalance: an experimental and modeling study
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electronic nose polymer-carbon composite molecular↗
Effect of layer bending on montmorillonite hydration and structure from molecular simulation
Conceptual models of smectite hydration include planar (flat) clay layers that undergo stepwise expansion as successive monolayers of water molecules fill the interlayer regions. However, X-ray diffraction (XRD) studies indicate the presence of interstratified hydration states, suggesting non-uniform interlayer hydration in smectites. Additionally, recent theoretical studies have shown that clay layers can adopt bent configurations over nanometer-scale lateral dimensions with minimal effect on mechanical properties. Therefore, in this study we used molecular simulations to evaluate structural properties and water adsorption isotherms for montmorillonite models composed of bent clay layers in mixed hydration states. Results are compared with models consisting of planar clay layers with interstratified hydration states (e.g. 1W–2W). The small degree of bending in these models (up to 1.5 Å of vertical displacement over a 1.3 nm lateral dimension) had little or no effect on bond lengths and angle distributions within the clay layers. Except for models that included dry states, porosities and simulated water adsorption isotherms were nearly identical for bent or flat clay layers with the same averaged layer spacing. Similar agreement was seen with Na- and Ca-exchanged clays. In conclusion, while the small bent models did not retain their configurations during unconstrained molecular dynamics simulation with flexible clay layers, we show that bent structures are stable at much larger length scales by simulating a 41.6×7.1 nm 2 system that included dehydrated and hydrated regions in the same interlayer.
58 GEOSCIENCES↗
Homogeneous ice nucleation in an ab initio machine-learning model of water
Molecular simulations have provided valuable insight into the microscopic mechanisms underlying homogeneous ice nucleation. While empirical models have been used extensively to study this phenomenon, simulations based on first-principles calculations have so far proven prohibitively expensive. Here, we circumvent this difficulty by using an efficient machine-learning model trained on density-functional theory energies and forces. We compute nucleation rates at atmospheric pressure, over a broad range of supercoolings, using the seeding technique and systems of up to hundreds of thousands of atoms simulated with ab initio accuracy. The key quantity provided by the seeding technique is the size of the critical cluster (i.e., a size such that the cluster has equal probabilities of growing or melting at the given supersaturation), which is used together with the equations of classical nucleation theory to compute nucleation rates. We find that nucleation rates for our model at moderate supercoolings are in good agreement with experimental measurements within the error of our calculation. We also study the impact of properties such as the thermodynamic driving force, interfacial free energy, and stacking disorder on the calculated rates.
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