Score Dynamics: Scaling Molecular Dynamics with Picoseconds Time Steps via Conditional Diffusion Model
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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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Implemented advance Surface-Hopping functional in time dependent Density Functional Theory (TDDFT) simulator package for modeling of nonlinear X-ray spectroscopy in complex molecular materials
Abstract not provided.
74 ATOMIC AND MOLECULAR PHYSICS↗
Microscale Modeling of Energetic Materials with Strength & Plasticity Informed by Molecular Dynamics.
Abstract not provided.
Damm, David↗
Results of the SPAS-01 RCS plume impingement test
The Orbiter RCS flowfield is modeled in the free molecular region and an impingement analysis is performed to characterize on-orbit plume interactions with nearby bodies, such as the SPAS-01 satellite. The flow in the far field is treated with a source flow model at any point 40 ft from the nozzle. Impingement is calculated according to Newtonian impact theory. The flow density in the far field is expressed in terms of a conservation of energy equation, with account taken of the expansion of the gas, cooling, and the angular distribution. The SPAS-01 platform gathered data at eight different positions around the Orbiter to test the model predictions. Excellent agreement was obtained in predictions of the torque and rotation rate induced on the SPAS by firing the thrusters.
Lazaron, M. P.↗
A predictive model for failure properties of thermoset resins
A predictive model for the three-dimensional failure behavior of engineering polymers has been developed in a recent NASA-sponsored research program. This model acknowledges the underlying molecular deformation mechanisms and thus accounts for the effects of different chemical compositions, crosslink density, functionality of the curing agent, etc., on the complete nonlinear stress-strain response including yield. The material parameters required by the model can be determined from test-tube quantities of a new resin in only a few days. Thus, we can obtain a first-order prediction of the applicability of a new resin for an advanced aerospace application without synthesizing the large quantities of material needed for failure testing. This technology will effect order-of-magnitude reductions in the time and expense required to develop new engineering polymers.
Caruthers, James M.↗
Review of the Effects of Polymer Binder Properties on Microstructure and Irreversible Volume Growth of Plastic Bonded Explosives Formulations
The rational design of effective polymeric binders for the formulation of plastic-bonded explosives (PBX) is challenging due to their inherent compositional complexity. The composites comprise irregularly shaped energetic material (EM) powders coated with low weight fractions of polymer via non-equilibrium processes such as slurry coating. Defects can deleteriously affect PBX stability and performance: nano- to micrometer-scale voids can act as loci for hot spots, lowering deflagration and detonation temperatures in unpredictable ways. Furthermore, some nominally desirable polymer properties are at odds with each other: e. g. good flow characteristics are desirable for coating and adhesion, but mechanical stiffness is needed to prevent deformation and cracking of PBX under mechanical stress. Good binder adhesion is critical, but the best means to predict and measure adhesion in PBX is not obvious. Experimental methods of determining binder adhesion on model surfaces may not capture polymer structural configurations relevant to deposition during coating. Molecular dynamics-based computational models have predicted key observables in PBX formulation, suggesting that they may be powerful tools for binder selection. In this review, primarily recent (~2006 and later) literature on polymeric binders for insensitive HE (IHE) is surveyed. We focus on how binder properties influence observable PBX properties as resistance to irreversible volume growth and void formation in PBX formulations mainly (but not exclusively) featuring 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) as the EM (e. g. PBX 9502, LX-17 and others). Conclusions from these studies yield useful guidelines for choosing HE binder candidates, as well as for the general design of highly-filled polymer composite materials. Finally, studies describing challenges in PBX formulation with the newer and more energetically dense high explosive, LLM-105, will be discussed.
adhesion↗
ACES-GNN: can graph neural network learn to explain activity cliffs?
Graph Neural Networks (GNNs) have revolutionized molecular property prediction by leveraging graph-based representations, yet their opaque decision-making processes hinder broader adoption in drug discovery. This study introduces the Activity-Cliff-Explanation-Supervised GNN (ACES-GNN) framework, designed to simultaneously improve predictive accuracy and interpretability by integrating explanation supervision for activity cliffs (ACs) into GNN training. ACs, defined by structurally similar molecules with significant potency differences, pose challenges for traditional models due to their reliance on shared structural features. By aligning model attributions with chemist-friendly interpretations, the ACES-GNN framework bridges the gap between prediction and explanation. Validated across 30 pharmacological targets, ACES-GNN consistently enhances both predictive accuracy and attribution quality for ACs compared to unsupervised GNNs. Our results demonstrate a positive correlation between improved predictions and accurate explanations, offering a robust and adaptable framework to better understand and interpret ACs. This work underscores the potential of explanation-guided learning to advance interpretable artificial intelligence in molecular modeling and drug discovery.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
The Modified Void Nucleation and Growth Model (MNAG) for Damage Evolution in BCC Ta
A void coalescence term was proposed as an addition to the original void nucleation and growth (NAG) model to accurately describe void evolution under dynamic loading. The new model, termed as modified void nucleation and growth model (MNAG model), incorporated analytic equations to explicitly account for the evolution of the void number density and the void volume fraction (damage) during void nucleation, growth, as well as the coalescence stage. The parameters in the MNAG model were fitted to molecular dynamics (MD) shock data for single-crystal and nanocrystalline Ta, and the corresponding nucleation, growth, and coalescence rates were extracted. The results suggested that void nucleation, growth, and coalescence rates were dependent on the orientation as well as grain size. Compared to other models, such as NAG, Cocks–Ashby, Tepla, and Tonks, which were only able to reproduce early or later stage damage evolution, the MNAG model was able to reproduce all stages associated with nucleation, growth, and coalescence. The MNAG model could provide the basis for hydrodynamic simulations to improve the fidelity of the damage nucleation and evolution in 3-D microstructures.
36 MATERIALS SCIENCE↗
A Multiscale Cohesive Law for Carbon Fiber Networks
Better predictive models of mechanical failure in low-weight heat shield composites would aid material certification for missions with aggressive atmospheric entry conditions. Here, we develop such a model for the rapid engineering analysis of the failure limits of phenolic impregnated carbon ablator (PICA) - a leading heat shield material whose structural component is a carbon fiber network. We hypothesize inelastic deformation failure mechanisms and model their behavior using molecular dynamics simulations to calculate the binding energy. We then upscale this binding energy to the macroscale using a renormalization argument. The approach delivers insightful and reasonably accurate macroscale predictions that compare favorably to experiments. In application, the model is validated for a particular variety of PICA by comparison to experiment and would then be used to study design scenarios in different entry conditions.
W. SCHILL↗
Atoms and interatomic bonding synergism inherent in molecular electronic wave functions
The chemical model of matter consists of atoms held together by bonds. The success of this model implies that the physical interactions of the electrons and nuclei in molecules combine into compound interactions that create the bonding. In the quantum mechanical description, the modified atoms in molecules and the bonding synergism are contained in the molecular electronic wave function. So far, only part of this information has been recovered from the wave function. Notably, the atoms have remained unidentified in the wave function. One reason is that conventional energy decomposition analyses formulate separate model wave functions, independent of the actual wave function, to represent “prepared atoms” and preconceived interactions and, then, intuitively catenate the parts. In the present work, the embedded modified atoms and the inherent physical synergisms between them are determined by a unified derivation entirely from the actual molecular valence space wave function. By means of a series of intrinsic orbital and configurational transformations of the wave function, the energy of formation of a molecule is additively resolved in terms of intra-atomic energy changes, interference energies, and quasi-classical, non-classical, and charge-transfer Coulombic interactions. Here, the analysis furnishes an algorithm for the quantitative resolution of the energy of formation, which enables analyses elucidating reaction energies.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Modeling of pressure-induced far-infrared absorption spectra Molecular hydrogen pairs
Meyer et al. (1985) have calculated the accurate induced dipole moment function of H2-H2 from first principles, using highly correlated wave functions for the first time in such work. The present paper is concerned with the collision-induced translational-rotational absorption coefficient for molecular hydrogen pairs, taking into account computations on the basis of the fundamental theory considered by Meyer et al. Data have been obtained for temperatures in the range from 40 to 300 K. Criteria are developed for choosing among various model line shapes. It is found that certain models are capable of approximating the quantum profiles closely, with rms errors of only a few percent.
Borysow, J.↗
A model for the CI (609 micron) emission of Orion
A numerical model energy balance and chemical equilibrium in the photodissociation regions at the edge of molecular clouds is presented. The model is used to calculate the emergent intensities of the following fine-structure lines: OI (at 63, 145 microns); CI (at 609, 370 microns); C II (at 158 microns); and the low-lying rotational transitions of CO. It is shown that column densities in the range 2 x 10 to the 17th to 2 x 10 to the 18th per sq cm can be obtained for the C(+)/C/CO transition region at the edges of molecular clouds. The difference in the column densities is attributed to changes in the charge exchange reactions of C(+) with SiO and S, and to the process of carbon self-healing. It is found that the calculations are in good agreement with the observed conditions in the photodissociation regions behind Orion (1) C Ori, and near the surface of OMC 1.
Tielens, A. G. G. M.↗
Advancing energy storage through solubility prediction: leveraging the potential of deep learning
Solubility prediction plays a crucial role in energy storage applications, such as redox flow batteries, because it directly affects the efficiency and reliability. Researchers have developed various methods that utilize quantum calculations and descriptors to predict the aqueous solubilities of organic molecules. Notably, machine learning models based on descriptors have shown promise for solubility prediction. As deep learning tools, graph neural networks (GNNs) have emerged to capture complex structure–property relationships for material property prediction. Specifically, MolGAT, a type of GNN model, was designed to incorporate n-dimensional edge attributes, enabling the modeling of intricacies in molecular graphs and enhancing the prediction capabilities. In a previous study, MolGAT successfully screened 23 467 promising redox-active molecules from a database of over 500 000 compounds, based on redox potential predictions. This study focused on applying the MolGAT model to predict the aqueous solubility (log S) of a broad range of organic compounds, including those previously screened for redox activity. The model was trained on a diverse sample of 8494 organic molecules from AqSolDB and benchmarked against literature data, demonstrating superior accuracy compared with other state of the art graph-based and descriptor-based models. Subsequently, the trained MolGAT model was employed to screen redox-active organic compounds identified in the first phase of high-throughput virtual screening, targeting favorable solubility in energy storage applications. The second round of screening, which considered solubility, yielded 12 332 promising redox-active and soluble organic molecules suitable for use in aqueous redox flow batteries. Thus, the two-phase high-throughput virtual screening approach utilizing MolGAT, specifically trained for redox potential and solubility, is an effective strategy for selecting suitable intrinsically soluble redox-active molecules from extensive databases, potentially advancing energy storage through reliable material development. This indicates that the model is reliable for predicting the solubility of various molecules and provides valuable insights for energy storage, pharmaceutical, environmental, and chemical applications.
25 ENERGY STORAGE↗
Grazer-induced changes in molecular signatures of cyanobacteria
Algal biomass production is an emerging renewable source of fuels, nutrients, manufacturing materials, and pharmaceuticals. Industrial-scale production is predominantly performed in open raceway ponds that are inexpensive to build, operate, and maintain compared to closed bioreactors. However, these open pond systems suffer from increased opportunities for biological contamination from predators, pathogens, and competitors, which result in reduced biomass quality and yields and often the complete destruction of the crop over a short period of time. Early detection of contaminants is a necessary step of integrated pest management for triggering and informing interventions to prevent crop losses. To develop a sensitive method of detection utilizing mass spectrometry (MS), we used three methods – imaging mass spectrometry (imaging MS), liquid chromatography MS/MS (LC-MS/MS) combined with molecular networking, and gas chromatography MS/MS (GC–MS/MS) – to observe and identify molecular signatures from a model predator-prey system of the heterolobosean amoeba HGG1 preying on the filamentous cyanobacterium Anabaena sp. PCC 7120. Imaging MS enabled the association of molecules with the crop, the predator, or the activity of protozoan grazing, while LC-MS/MS-based molecular networking identified a subset of the grazing-specific signals as the chlorophyll breakdown products pyropheophytin, pheophorbide A, and pyropheophorbide. Application of MS techniques to other amoebacyanobacterial predator-prey pairs allowed categorization of grazing signatures as universal or specific to the species of prey, predator, or the prey-predator pairing, indicating that MS-based techniques can distinguish crops from competitors and potentially identify predators. Finally, GCMS/MS was shown to be capable of monitoring novel volatile organic compounds (VOCs), including those predicted to be released through chlorophyll breakdown, in the headspace over algal cultures under predation. Furthermore, these results demonstrate that the combination of multiple MS technologies creates a predictive framework to identify and catalog relevant molecular signatures for informing crop protection strategies.
09 BIOMASS FUELS↗
Accessing the triplet manifold of naphthalene benzimidazole–phenanthroline in rhenium(i) bichromophores
In this work, the steady-state and ultrafast to supra-nanosecond excited state dynamics of fac-[Re(NBI-phen)(CO) 3 (L)](PF 6 ) (NBI-phen = 16H-benzo[4',5']isoquinolino[2',1':1,2]imidazo[4,5-f][1,10]phenanthrolin-16-one) as well as their respective models of the general molecular formula [Re(phen)(CO) 3 (L)](PF 6 ) (L = PPh 3 and CH 3 CN) has been investigated using transient absorption and time-gated photoluminescence spectroscopy. The NBI-phen containing molecules exhibited enhanced visible light absorption with respect to their models and a rapid formation (<6 ns) of the triplet ligand-centred (LC) excited state of the organic ligand, NBI-phen. These triplet states exhibit an extended excited state lifetime that enable the energized molecules to readily engage in triplet–triplet annihilation photochemistry.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
One Electron Multiple Proton Transfer in Model Organic Donor–Acceptor Systems: Implications for High-Frequency EPR
EPR spectroscopy is an important spectroscopic method for identification and characterization of radical species involved in many biological reactions. The tyrosyl radical is one of the most studied amino acid radical intermediates in biology. Often in conjunction with histidine residues, it is involved in many fundamental biological electron and proton transfer processes, such as in the water oxidation in photosystem II. As biological processes are typically extremely complicated and hard to control, molecular bio-mimetic model complexes are often used to clarify the mechanisms of the biological reactions. In this work, we present theoretical calculations to investigate the sensitivity of magnetic resonance parameters to proton-coupled electron transfer events, as well as conformational substates of the molecular constructs which mimic the tyrosine-histidine (Tyr-His) pairs found in a large variety of proteins. Upon oxidation of the phenol, the Tyr analog, these complexes can perform not only one-electron one-proton transfer (EPT), but also one-electron two-proton transfers (E2PT). It is shown that in aprotic environment the g X -components of the electronicg-tensor are extremely sensitive to the first proton transfer from the phenoxyl oxygen to the imidazole nitrogen (EPT product), leading to a significant increase of the g X -value of up to 0.003, but are not sensitive to the second proton transfer (E2PT). In the latter case, the change of the g X -value is much smaller (ca. 0.0001), which is too small to be distinguished even by high-frequency EPR. The 14 N hyperfine values are also too similar to allow differentiation between the different protonation states in EPT and E2PT. The magnetic resonance parameters were also calculated as a function of the rotation angles around single bonds. It was demonstrated that rotation of the phenoxyl group results in large positive changes (> 0.001) in the g X -values. Analysis of the data reveals that the main source of these changes is related to the strength of the H-bond between phenoxyl oxygen and the proton(s) on N 1 and N 2 positions of the imidazole.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Composition and radiative properties of grains in molecular clouds and accretion disks
We define a model of the compositon and abundances of grains and gases in molecular cloud cores and accretion disks around young stars by employing a wide range of astronomical data and theory, the composition of primitive bodies in the solar system, and solar elemental abundances. In the coldest portions of these objects, we propose that the major grain species include olivine (Fe, Mg, 2SiO4), orthopyroxene (Fe, Mg, SiO3), volatile and refractory organics, water ice, troilite (FeS), and metallic iron. This compositional model differs from almost all previous models of the interstellar medium (ISM) by having organics as the major condensed C species, rather than graphite; by including troilite as a major grain species; and by specifying the mineralogical composition of the condensed silicates. Using a combination of laboratory measurements of optical constants and asymptotic theory, we derive values of the real and imaginary indices of refraction of these grain species over a wavelength range that runs from the vacuum ultraviolet (UV) to the radio domain. The above information on grain properties is used to estimate the Rosseland mean opacity of the grains and their monochromatic opacity.
Pollack, James B.↗
Emergence of a replicating species from an in vitro RNA evolution reaction
The technique of self-sustained sequence replication allows isothermal amplification of DNA and RNA molecules in vitro. This method relies on the activities of a reverse transcriptase and a DNA-dependent RNA polymerase to amplify specific nucleic acid sequences. We have modified this protocol to allow selective amplification of RNAs that catalyze a particular chemical reaction. During an in vitro RNA evolution experiment employing this modified system, a unique class of "selfish" RNAs emerged and replicated to the exclusion of the intended RNAs. Members of this class of selfish molecules, termed RNA Z, amplify efficiently despite their inability to catalyze the target chemical reaction. Their amplification requires the action of both reverse transcriptase and RNA polymerase and involves the synthesis of both DNA and RNA replication intermediates. The proposed amplification mechanism for RNA Z involves the formation of a DNA hairpin that functions as a template for transcription by RNA polymerase. This arrangement links the two strands of the DNA, resulting in the production of RNA transcripts that contain an embedded RNA polymerase promoter sequence.
Non-NASA Center↗