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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Machine Learning Models for Binary Molecular Classification using VUV Absorption Spectra

Machine learning methods were combined with differential absorption spectroscopy measurements in the vacuum-ultraviolet region (5.167 – 9.920 eV) in order to develop predictive capabilities for inferring molecular structure from the spectra. Several types of species were analyzed and, for modeling purposes, were defined using a single classification: (1) alkane, (2) conjugation with oxygen (e.g. diacetyl, ethyl vinyl ether), (3) non-conjugated alkene (e.g. 1-butene, 1,4-cyclohexadiene), (4) oxygen-containing (e.g. 1-butanol, tetrahydrofuran), or (5) cyclic (e.g. cyclopentane, cyclohexanone). The latter molecular classification excluded cyclic ethers. Several modeling methods were employed in the analysis of 102 absorption spectra, 24 of which were measured for the first time. The primary objective was to identify suitable methods that enable accurate predictions of molecular structure classifications with minimized statistical uncertainties. Rather than identifying a single, unifying method to reliably predict molecular structure contributions to VUV absorption spectra, coordination is required among a particular method, the type of molecular structure detail (e.g. conjugation), and absorption region of interest. The latter is accomplished using a binning approach, wherein absorption regions of ~0.5 eV were utilized rather than the entire ~4.8 eV range. Photon energy binning enabled analysis of region-specific predictions of accuracy, precision, and recall. The outcome from the binning approach is that, rather than utilizing the entire spectrum, optimal determination of molecular structure using machine learning methods depends on the absorption region. Furthermore, the present work provides separate machine learning models for each molecular classification, which enables the identification of multi-functional species relevant to atmospheric chemistry and combustion chemistry, where isomer-resolved speciation is critical to understanding complex reaction networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1993

Molecular line emission models of Herbig-Haro objects. II - HCO(+) emission

We present time-dependent models of the chemistry and temperature of interstellar molecular gas clumps that are exposed to the radiation from propagating stellar-jet shocks. The X-ray, EUV, and FUV radiation from the shock initiates ion chemistry and also heats the gas in the clumps. Using representative parameters, we show that, on the shock transit time between the clumps, the abundances of the ionized molecular species that are produced in the clumps can exceed the values determined from steady state models by several orders of magnitude. Collisional excitation by the heated gas can lead to measurable line emission from several ionized species; as in previous investigations of X-ray-irradiated molecular gas, we find that electron impacts contribute significantly to this process. We apply these results to the interpretation of the HCO(+) line emission that has already been detected in several Herbig-Haro objects. We demonstrate that this picture provides a natural explanation of the fact that the line intensity typically peaks ahead of the associated shock, as well as of the reported low line-center velocities and narrow line widths. We tabulate several diagnostic line intensities of HCO(+) and other molecular species that may be used to infer the physical conditions in the emitting gas.

Wolfire, Mark G.↗
NASA NTRS1984

A theoretical model for calculation of molecular stopping power

A modified local plasma model is established. The Gordon-Kim's molecular charged density model is employed to obtain a formula to evaluate the stopping power of many useful molecular systems. The stopping power of H2 and He gas was calculated for incident proton energy ranging from 100 keV to 2.5 MeV. The stopping power of O2, N2, and water vapor was also calculated for incident proton energy ranging from 40 keV. to 2.5 MeV. Good agreement with experimental data was obtained. A discussion of molecular effects leading to department from Bragg's rule is presented. The equipartition rule and the effect of nuclear momentum recoiling in stopping power are also discussed.

Xu, Y. J.↗
NASA NTRS1993

The embedded young stars in the Taurus-Auriga molecular cloud. II - Models for scattered light images

We describe NIR imaging observations of embedded young stars in the Taurus-Auriga molecular cloud. We find a large range in J-K and H-K colors for these class I sources. The bluest objects have colors similar to the reddest T Tauri stars in the cloud; redder objects lie slightly above the reddening line for standard ISM dust and have apparent K extinctions of up to 5 mag. Most of these sources also show extended NIR emission on scales of 10-20 arcsec which corresponds to linear sizes of 1500-3000 AU. The NIR colors and nebular morphologies for this sample and the magnitude of linear polarization in several sources suggest scattered light produces most of the NIR emission in these objects. We present modeling results that suggest mass infall rates that agree with predictions for cold clouds and are generally consistent with rates estimated from radiative equilibrium models. For reasonable dust grain parameters, the range of colors and extinctions require flattened density distributions with polar cavities evacuated by bipolar outflows. These results support the idea that infall and outflow occur simultaneously in deeply embedded bipolar outflow sources. The data also indicate fairly large centrifugal radii and large inclinations to the rotational axis for a typical source.

Kenyon, Scott J.↗
DOE OSTI2025

Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge

Understanding the interactions and regulatory relationships among biomolecules is essential for deciphering complex biological systems and elucidating the mechanisms behind diverse biological functions. Traditionally, the collection of such molecular interaction data has relied on expert curation, a process that is both time-consuming and labor-intensive. To address these limitations, this study explores the use of large language models (LLMs) to automate the genome-scale extraction of molecular interaction knowledge. Here, we evaluate the performance of various LLMs on key biological tasks, including the identification of protein-protein interactions, detection of genes associated with pathways influenced by low-dose radiation, and inference of gene regulatory relationships. Our findings demonstrate that larger LLMs tend to perform better, particularly in extracting intricate gene and protein interactions. Despite their strengths, these models face challenges in recognizing functionally diverse gene groups and highly correlated regulatory relationships. Through a comprehensive analysis using established molecular interaction and pathway databases, we show that LLMs possess the potential to identify relevant biomolecules and predict their interactions, offering valuable insights and marking a significant step toward AI-driven biological knowledge discovery.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2023

Development of a machine-learning-based ionic-force correction model for quantum molecular dynamic simulations of warm dense matter

In this work Δ learning is used to map orbital-free density functional theory (OF-DFT) ionic forces to the corresponding Kohn-Sham (KS) DFT ionic forces. The development of the approximate force difference in terms of the ion positions is constructed and serves as a stand in for the ground truth force difference. Descriptor vectors for ion configurations are constructed using all distance between ions in conjunction with an indexing based on a nearest neighbor ranking. It is demonstrated that such a scheme of descriptors can uniquely describe an ionic configuration up to a rotation and reflection when no ambiguity in the nearest neighbor ranking exists. How to handle the case when an ambiguity exists in the nearest neighbor ranking is discussed. As a proof of principle, the model is trained and tested on warm dense hydrogen at temperatures between 1 and 15 eV. Once tested, the model was used to perform molecular dynamic simulations of warm dense hydrogen. Furthermore, the resulting energies and pressures are within 1% and 2% of their respective target KS values.

36 MATERIALS SCIENCE↗
NASA NTRS1993

The embedded young stars in the Taurus-Auriga molecular cloud. I - Models for spectral energy distributions

We describe radiative transfer calculations of infalling, dusty envelopes surrounding pre-main-sequence stars and use these models to derive physical properties for a sample of 21 heavily reddened young stars in the Taurus-Auriga molecular cloud. The density distributions needed to match the FIR peaks in the spectral energy distributions of these embedded sources suggest mass infall rates similar to those predicted for simple thermally supported clouds with temperatures about 10 K. Unless the dust opacities are badly in error, our models require substantial departures from spherical symmetry in the envelopes of all sources. These flattened envelopes may be produced by a combination of rotation and cavities excavated by bipolar flows. The rotating infall models of Terebey et al. (1984) models indicate a centrifugal radius of about 70 AU for many objects if rotation is the only important physical effect, and this radius is reasonably consistent with typical estimates for the sizes of circumstellar disks around T Tauri stars.

Kenyon, Scott J.↗
DOE OSTI2022

Developing Reaction Chemistry Models from Reactive Molecular Dynamics: TATB

Reactive Molecular Dynamic (RMD) are used to simulate the cook-off chemistry of TATB at a variety of fixed density and fixed temperature conditions. The chemical transformations are monitored using a Coordination Geometry Analysis (CGA) approach which tracks which atom types are bonded to each specific atom. This particularly identifies oxidation state changes that occur during the transformations. Correlations between these different chemical changes are identified using a Non-negative Matrix Factorization (NMF) approach. These identify reduced order chemistry models for the TATB system which contains six components whose concentration profiles are a function of both the temperature and density/pressure. The time histories of these transformations appear to show exponential growth/decay properties that could be fit with Arrhenius rates. These components should form the basis of deflagration rate models for these materials which could then be used in mesoscale simulations to analyze accidental initiation, shock-to-detonation and detonation propagation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1975

AESC multinodal free molecular contamination transport model

The film formation effects are examined of self-induced clouds and condensed films of volatile condensible materials (VCMs) which outgas from such sources as paints, coatings, adhesives, and other nonmetallic surfaces of satellites. A semi-empirical theory for the multiple scattering of free neutral molecules from dielectric surfaces which includes the effects of condensation and evaporation is defined and a mathematical model of the process is developed. The formation rate of thin surface films is considered to be the net accumulation of two simultaneous molecular processes; emission (evaporation) and adsorption (condensation). The net formation rate is considered to be dependent upon the temperature of the surface nodes, the mass and temperatures of the gas molecules, and the total pressure of the incident molecular flux. Apparatus and procedures for measuring these parameters are presented.

Zeiner, E. A.↗
DOE OSTI2024

Surfactant-Specific AI-Driven Molecular Design: Integrating Generative Models, Predictive Modeling, and Reinforcement Learning for Tailored Surfactant Synthesis

Molecular design is a critical aspect of various scientific and industrial fields, where the properties of molecules hold significant importance. In this study, a 3-fold methodology design is presented that leverages the power of generative artificial intelligence (AI), predictive modeling, and reinforcement learning to create tailored molecules with desired properties. This model synergistically combines deep learning techniques with Self-Referencing Embedded Strings (SELFIES) molecular representation to build a generative model that generates valid molecules and a graphical neural network model that accurately forecasts molecular properties. The Variational Autoencoder (VAE) coupled with reinforcement learning helps refine molecule generation based on targeted attributes. Data from an experimental study involving surfactants were used to test the framework. A validation of the structural integrity of the molecules generated was conducted, and Tanimoto similarities were used to quantify the similarity and diversity between the original and generated molecular structures. Also, saliency maps for the generated surfactants were produced to identify the features explaining the property values. Lastly, molecular dynamics simulations were used to validate the stability of the generated molecules. The results showed that the proposed framework can effectively produce valid molecules within the set property threshold value.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Mapping TpPa-1 covalent organic framework (COF) molecular interactions in mixed solvents via atomistic modeling and experimental study

Complex solvent environments continue to limit the widespread adoption of organic solvent nanofiltration (OSN) in many chemical industry applications. In this paper we employ a commercially available covalent organic framework (COF), TpPa-1, and force field models to molecularly map separation performance of TpPa-1 membrane in mixed solvents. To minimize time and length scale mismatch between atomistic modeling and experiments, solvent permeance was normalized with water in modeling and experimental results to enable direct comparison. Model outputs, such as organic solvent permeance and solute rejection rate, matched well with filtration results. Since the atomistic models assume that all mass transfer is via through-pore transport, the discrepancies between modeling and experimental results provide insights on the effect of linear polymer defects, adsorption and interstitial mass transfer on polycrystalline COF membrane performance. In sum, force field models can serve as digital twins of COF membranes to simulate separation processes while capturing the effects of COF structure, chemistry, and crystallinity on membrane performance in complex organic solvent environments. Finally, this approach will provide insight into future COF design and synthesis for persisting separation challenges.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1974

Model calculations for diffuse molecular clouds

A steady state isobaric cloud model is developed. The pressure, thermal, electrical, and chemical balance equations are solved simultaneously with a simple one dimensional approximation to the equation of radiative transfer appropriate to diffuse clouds. Cooling is mainly by CII fine structure transitions, and a variety of heating mechanisms are considered. Particular attention is given to the abundance variation of H2. Inhomogeneous density distributions are obtained because of the attenuation of the interstellar UV field and the conversion from atomic to molecular hyrodgen. The effects of changing the model parameters are described and the applicability of the model to OAO-3 observations is discussed. Good qualitative agreement with the fractional H2 abundance determinations has been obtained. The observed kinetic temperatures near 80 K can also be achieved by grain photoelectron heating. The problem of the electron density is solved taking special account of the various hydrogen ions as well as heavier ones.

Glassgold, A. E.↗
NASA NTRS1978

H2 cooling, dissociation, and infrared emission in shocked molecular clouds

Models are presented of interstellar shocks in molecular clouds over ranges of ambient molecular density from 1000 to 10 million per cu cm and shock velocity from 6 to 14 km/s. Estimates of H2-H2 collisional-excitation rates are used to derive the H2 radiative cooling rates from vibrational-rotational quadrupole transitions as a function of n(H2) and temperature. The emissivities integrated through the shock of the strongest infrared lines in the v = 1-0, 2-0, and 2-1 bands of H2. The effectiveness of H2 dissociative cooling is considered for the highest-velocity shocks. The H2 line intensities from such shocks are compared with those produced by the 'competitive' mechanism of UV pumping for two likely driving mechanisms of shocks - wind-driven shells and expanding H II regions.

Shull, J. M.↗
DOE OSTI2023

Machine Learning Models for Predicting Molecular UV–Vis Spectra with Quantum Mechanical Properties

Accurate understanding of Ultraviolet–visible (UV–Vis) spectra is critical for highthroughput design of compounds for drug discovery. Experimentally determining UV–Vis spectra can become expensive when dealing with a large quantity of novel molecules. This provides us an opportunity to drive computational advances in molecular property predictions using quantum mechanics and machine learning. In this work, we use both Quantum Mechanically (QM) predicted and measured UV–Vis spectra as input to modify four different machine learning architectures: UVvis-SchNet, UVvis- DTNN, UVvis-Transformer, and UVvis-MPNN. Here we find that the UVvis-MPNN model outperforms the other models when using optimized 3D coordinates and QM predicted spectra as input features. This model has the highest performance for predicting UVVisible spectra with a training RMSE of 0.06 and validation RMSE of 0.08.

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

Application of molecular convergent close-coupling cross sections in a collisional radiative model for the triplet system of molecular hydrogen

Collisional radiative (CR) models for molecular hydrogen are of high relevance for performing qualitative and quantitative analysis of excited-state population densities measured in plasmas or predicting the dependence of plasma emission on parameter variations. Although the development of such models for H 2 started decades ago, major uncertainties still exist regarding the most important set of input parameters, namely the cross sections for electron-impact excitation. The deviations between cross sections from different datasets are particularly pronounced in the energy region close to the threshold energy, strongly increasing the uncertainty of CR models applied to low-temperature plasmas. This paper presents experimental validation of a set of newly calculated non ro-vibrationally resolved electron-impact cross sections calculated for the triplet system of H 2 using the molecular convergent close-coupling method in the adiabatic-nuclei formulation. These cross sections are implemented into a CR model based on the flexible solver Yacora. A first comparison of CR calculations with the different datasets to experimentally-determined population densities is performed at a planar ICP discharge for varying pressure (between 1 and 10 Pa) and RF power (between 700 and 1100 W). For the experimentally-accessible electron temperature and density range (2.5–10 eV and 1.8–3.3 × 10 16 m –3 , respectively), very good agreement between the model and experiment is obtained using the new data set, in contrast to previously used cross sections.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗