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This paper presents a simple physical model for the bipolar molecular outflows that frequently accompany star formation. The model forges an intrinsic link between the bipolar flow phenomenon and the process of star formation, and it helps to explain many of the systematics known for existing sources.
In this lecture I will discuss the many different manifestation of interstellar dust, and current dust models that satisfy interstellar extinction, diffuse infrared emission, and interstellar abundances constraints. Dust is made predominantly in AGB stars and Type I1 supernovae, and I will present observational evidence for the presence of dust in these sources. I will then present chemical evolution models that follow the abundance of dust which is determined by the combined processes of formation, destruction by interstellar shock waves, and accretion in molecular clouds. The model will be applied to the evolution of PAHs and the evolution of dust in the high-redshift galaxy (z=6.42) JD11.
Colloidal particles with mobile binding molecules constitute a powerful platform for probing the physics of self-assembly. Binding molecules are free to diffuse and rearrange on the surface, giving rise to spontaneous control over the number of droplet–droplet bonds, i.e., valence, as a function of the concentration of binders. This type of valence control has been realized experimentally by tuning the interaction strength between DNA-coated emulsion droplets. Optimizing for valence two yields droplet polymer chains, termed ‘colloidomers’, which have recently been used to probe the physics of folding. To understand the underlying self-assembly mechanisms, here we present a coarse-grained molecular dynamics (CGMD) model to study the self-assembly of this class of systems using explicit representations of mobile binding sites. Further, we explore how valence of assembled structures can be tuned through kinetic control in the strong binding limit. More specifically, we optimize experimental control parameters to obtain the highest yield of long linear colloidomer chains. Subsequently tuning the dynamics of binding and unbinding via a temperature-dependent model allows us to observe a heptamer chain collapse into all possible rigid structures, in good agreement with recent folding experiments. Our CGMD platform and dynamic bonding model (implemented as an open-source custom plugin to HOOMD-Blue) reveal the molecular features governing the binding patch size and valence control, and opens the study of pathways in colloidomer folding. This model can therefore guide programmable design in experiments.
Paraffin wax is often used as a nuclear moderator to slow down the fast neutrons in experimental critical assemblies [1]. It is a colorless and soft solid material that consists primarily of straight-chain alkanes (n-alkanes), which are hydrocarbons with the general formula CnH2n+2 [2-3]. The length of the hydrocarbon chain ranges from C20 to C30 and higher [2]. It is distinguished by its solid state at room temperature and begins to melt above approximately 310 K [4]. Paraffin wax is a commonly employed substance in the manufacture of shielding. One of its noteworthy characteristics is its ability to effectively absorb the neutrons. Also, it possesses a high macroscopic cross section, which enables it to efficiently moderate neutrons. As a result, paraffin wax is extensively utilized in various applications where moderation and shielding of neutrons are needed. For simulations, it is necessary to evaluate its thermal scattering law (TSL) and cross sections. Computationally, classical molecular dynamics (CMD) simulations provide the capability of simulating atomic details. For example, several unary, binary, and few multi component mixtures have been investigated of the paraffin model by using molecular dynamics simulations [5-12]. An assessment of thermal neutron scattering in a heavy paraffinic oil treated both as a solid and a viscous fluid containing 25% linear branched paraffin (C30H62), 35% one ring cycloalkane (C30H60), 15% two rings cycloalkane (C30H58), and 25% aromatic (C30H60) chains has been studied using CMD simulations for producing TSL data [13]. Nevertheless, there is lack of TSL and cross section data for paraffin wax as most of the reported analyses focus on the unary and binary mixture of n-alkanes, which is not consistent with actual paraffin wax [2]. In this work, we applied the equilibrium CMD simulations technique to explore the structure and dynamical properties of wax, which are fundamental input to calculate the TSL. A paraffin wax system was modeled using the CMD code LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator) [14-15] with the semi-empirical COMPASS [16] force field. The density of state (DOS) was calculated from the normalized velocity autocorrelation function (VACF), which is the Fourier transform of the normalized VACF. The DOS was used for the calculation of the TSL and thermal scattering cross sections. The paraffin wax atomic system was constructed by using the MedeA material design platform [17], and was benchmarked using available properties (i.e., density, bond lengths, angles, diffusivity, and viscosity).
Theoretical considerations and analysis of the results of gamma ray astronomy suggest that the galactic cosmic rays are dynamically coupled to the interstellar matter through the magnetic fields, and hence the cosmic ray density should be enhanced where the matter density is greatest on the scale of galactic arms. This concept has been explored in a galactic model using recent 21 cm radio observations of the neutral hydrogen and 2.6 mm observations of carbon monoxide, which is considered to be a tracer of molecular hydrogen. The model assumes: (1) cosmic rays are galactic and not universal; (2) on the scale of galactic arms, the cosmic ray column (surface) density is proportional to the total interstellar gas column density; (3) the cosmic ray scale height is significantly larger than the scale height of the matter; and (4) ours is a spiral galaxy characterized by an arm to interarm density ratio of about 3:1.
It is well-known that water in liquid and vapor phases exhibits weak visible-light absorption. Recent experiments, however, show that at the liquid-air interface, absorption drastically increases, accelerating evaporation beyond thermal limits by 2–5 times. Strikingly, evaporation peaks at green wavelengths, despite no corresponding absorptance peak. The underlying mechanism of this observation, termed the photomolecular effect, remains puzzling, particularly as water molecules do not exhibit resonance peaks in the visible spectrum. Here, we present a theoretical model explaining the effect. We show that surface-bound water clusters undergo non-resonant photon-driven evaporation, with green light being particularly effective. Crucially, we do not expect green light to couple more strongly than other wavelengths to the molecules at the surface, rather the energy of green light is more effectively used to vaporize water. Our model accounts for why the evaporation peak does not align with absorptance and provides a quantum mechanical explanation of how a single photon can vaporize an entire molecular cluster. This model challenges conventional views on water-light interactions, revealing a fundamentally non-thermal evaporation mechanism. Furthermore, our findings have implications for water purification, energy-efficient drying, and climate modeling, opening new pathways for optimizing evaporation-based technologies.
Drug discovery is a complex and challenging process, requiring the optimization of candidate compounds to identify those with the potential to become safe and effective drugs. Predicting molecular properties is an indispensable step in the drug discovery pipeline. Traditionally, this process is costly and time-intensive, involving multiple rounds of experiments and clinical trials, rendering it impractical for every candidate compound. Deep learning techniques have emerged as a promising approach to drug discovery to reduce the cost and time required to identify novel drugs. However, prevalent research in deep learning models focused on predicting molecular properties has primarily fixated on single-modal models, which utilize a single modality of data, neglecting the potential benefits of combining different data modalities. To overcome this limitation, we introduce MRL-Mol: a deep \textbf{M}ultimodal \textbf{R}epresentation \textbf{L}earning framework for accurate \textbf{Mol}ecular properties prediction. MRL-Mol harnesses three data modalities: sequence, graph, and image, augmenting the depth of comprehension. Leveraging a large-scale unlabeled dataset~($\sim$1M unique molecules), we pretrain MRL-Mol to extract inter- and intra-modal information. Our study demonstrates the superior performance of MRL-Mol in predicting molecular properties across six benchmark datasets, including both classification and regression tasks. Notably, MRL-Mol outperforms other state-of-the-art molecular properties prediction models. These findings suggest that by combining information from multiple data modalities, MRL-Mol can comprehend molecules better than single-modal deep learning models and identify molecular properties with better accuracy.
Spectroheliograms taken in the CN (0, 0) violet band near 3883 A show very small scale network and cell structures with high contrast. The bandhead itself, which is a broad feature due to overlap of several CN lines, allows the diagnostic simplicity of a continuum since motions, magnetic fields, and broadening mechanisms are unimportant. We have obtained spectroheliograms in the bandhead and center-to-limb photoelectric spectra of CN (0, 0) at Kitt Peak National Observatory. From the photoelectric spectra and a detailed analysis of the formation of the CN (0, 0) spectrum we derive a best-fit one-component upper photospheric model differing from that of the HSRA and recommend a change in solar carbon abundance from the HSRA value.
Analyses and numerical procedures are presented to investigate the radiative interactions in various energy transfer processes in gaseous systems. Both gray and non-gray radiative formulations for absorption and emission by molecular gases are presented. The gray gas formulations are based on the Planck mean absorption coefficient and the non-gray formulations are based on the wide band model correlations for molecular absorption. Various relations for the radiative flux and divergence of radiative flux are developed. These are useful for different flow conditions and physical problems. Specific plans for obtaining extensive results for different cases are presented. The procedure developed was applied to several realistic problems. Results of selected studies are presented.
Recent ion-induced nucleation studies in a binary mixture of H2O and H2SO4 vapors indicate that conventional classical nucleation theories cannot account for the experimentally observed ion-H2O-H2SO4 microcluster spectra. An empirical-analytical, semi-molecular theory of nucleation of a binary mixture of vapors on ions has been developed. This theory includes molecular approach to ion-induced changes in dielectric properties in the immediate neighborhood of an ion core as well as the size dependence of the surface tension of the microcluster. In addition to gaseous sulfuric acid and water molecules, the effect of ion-hydrates and sulfuric acid hydrates has also been considered. This theory is further simplified by using an empirical correction factor for the electrostatic term. This correction factor has been obtained by comparing the simplified model with the molecular dynamics calculations for selected ion hydrate formation. These two theoretical models are then compared with the classical theory for a variety of experimental conditions.
Techniques for the gas phase absorption of laser radiation for conversion to gas kinetic energy are discussed. Absorption by inverse Bremsstrahlung, in which laser energy is converted at a gas kinetic rate in a spectrally continuous process, is briefly described, and absorption by molecular vibrational rotation bands is discussed at length. High pressure absorption is proposed as a means of minimizing gas bleaching and dissociation, the major disadvantages of the molecular absorption process. A band model is presented for predicting the molecular absorption spectra in the high pressure absorption region and is applied to the CO molecule. Use of a rare gas seeded with Fe(CO)5 for converting vibrational modes to translation modes is described.
A concurrent multiscale modeling methodology that embeds a molecular dynamics (MD) region within a finite element (FEM) domain has been enhanced. The concurrent MD-FEM coupling methodology uses statistical averaging of the deformation of the atomistic MD domain to provide interface displacement boundary conditions to the surrounding continuum FEM region, which, in turn, generates interface reaction forces that are applied as piecewise constant traction boundary conditions to the MD domain. The enhancement is based on the addition of molecular dynamics-based cohesive zone model (CZM) elements near the MD-FEM interface. The CZM elements are a continuum interpretation of the traction-displacement relationships taken from MD simulations using Cohesive Zone Volume Elements (CZVE). The addition of CZM elements to the concurrent MD-FEM analysis provides a consistent set of atomistically-based cohesive properties within the finite element region near the growing crack. Another set of CZVEs are then used to extract revised CZM relationships from the enhanced embedded statistical coupling method (ESCM) simulation of an edge crack under uniaxial loading.
Production of vibrationally excited N 2 (N 2 (v)) in atmospheric pressure nonthermal plasma and loss of N 2 (v) by gas-phase reactions and reactions on catalytic surfaces are analyzed to examine the role of N 2 (v) in NH 3 formation by plasma catalysis. Vibrational state-to-state kinetic models complemented with molecular beam mass spectrometry (MBMS) measurements demonstrate that N 2 (v> 0) is produced with densities 100× greater than the density of N radicals by a radiofrequency atmospheric pressure plasma jet. The experimentally measured loss of N 2 (v) corresponds with a state-to-state kinetic model that describes loss of N 2 (v) by surface-mediated vibrational relaxation without consideration of reactions that convert N 2 (v) to NH 3 over the catalyst surface. Rate constants for vibrational relaxation of N 2 (v) on catalyst surfaces exceed upper bounds on proposed rate constants for NH 3 formation reactions from N 2 (v) over Fe when v < 9, Ni when v < 18, and Ag when v < 39, which indicates that only higher vibrational levels can possibly contribute to catalytic NH 3 formation faster than they undergo vibrational relaxation on the surface. Densities of N 2 (v> 8), vibrational levels that can possibly react over Fe to form NH 3 faster than they undergo vibrational relaxation, are less than or similar to N densities at the inlet of the catalyst bed and measured NH 3 formation for the investigated conditions in this work, while densities of N 2 (v> 17) and N 2 (v> 38) are orders of magnitude below the N density at the inlet of the catalyst bed and the measured NH 3 formation. The loss of N 2 (v) by vibrational relaxation on the surface limits the ability of N 2 (v) to contribute to catalytic NH 3 formation and explains why N 2 (v) does not produce NH 3 in quantities that are comparable to NH 3 formation from N even though N 2 (v > 0) is more abundantly produced by the plasma.
Here, a molecular dynamics simulation model was developed to comprehensively understand carbon dioxide over nitrogen (CO 2 /N 2 ) selectivity through polyphosphazene-based membrane comprising of poly[bis((methoxyethoxy)ethoxy)phosphazene] (MEEP) selective layer at the molecular level. The effects of temperature, pressure, and initial feed gas composition on the CO 2 transport on a polymer membrane were studied. The computed free energy and density profile of the permeating gas mixture exhibited that CO 2 molecules express higher interactions with the membrane than N 2 molecules, resulting in higher CO 2 /N 2 selectivity. Statistical analysis of gas molecules (CO 2 , water (H 2 O), and N 2 ) transportation suggested that hydro- and CO 2 -philic functional groups in the membrane significantly impact CO 2 permeability and CO 2 /N 2 selectivity. This study suggested that Lewis acid–base and hydrogen bonding combinations contribute to CO 2 permeation and CO 2 /N 2 selectivity. An equal CO 2 /N 2 selectivity was observed with and without water vapor in the feed gas suggesting that water does not hinder CO 2 transport through the membrane.
The need for sustainable power generation has increased interest in the use of hydrothermal fluids for industrial applications. New high-enthalpy geothermal systems and biowaste-to-fuel processes are two relevant examples that employ supercritical fluids which require an in-depth understanding of complex chemical reactions occurring near the supercritical temperature of water (374 °C). As these processes operate in thermodynamic regimes that are not currently covered by a standard molar Gibbs energy of formation model, only empirical fits for single reaction systems are available which limit the use of multi-component phase equilibria calculations that are standard practice for less extreme environments. Here, in this paper, we advance a standard molar Gibbs energy of formation model able to operate in these otherwise inaccessible thermodynamic states to include species needed for key mineral solubility systems and ion association reactions. This work extends a model based on molecular statistical thermodynamics (MST) into four new systems (Na 3 PO 4 -H 2 O, LiOH-H 2 O, KOH-H 2 O, and BaSO 4 -H 2 O) by extending the model to cover 10 new species. For each of these systems, model predictions were consistently within the experimental uncertainties for the new systems covered. A breakdown of MST contributions to the model revealed that electrostatic and hard sphere contributions were key to reproducing density dependencies of standard molar Gibbs energy of formation values around the critical point of water.
We present a method for dimensionally adaptive sparse trigonometric interpolation of multidimensional periodic functions belonging to a smoothness class of finite order. This method targets applications where periodicity must be preserved and the precise anisotropy is not known a priori. To the authors' knowledge, this is the first instance of a dimensionally adaptive sparse interpolation algorithm that uses a trigonometric interpolation basis. The motivating application behind this work is the adaptive approximation of a multi-input model for a molecular potential energy surface (PES) where each input represents an angle of rotation. Our method is based on an anisotropic quasi-optimal estimate for the decay rate of the Fourier coefficients of the model; a least-squares fit to the coefficients of the interpolant is used to estimate the anisotropy. Thus, our adaptive approximation strategy begins with a coarse isotropic interpolant, which is gradually refined using the estimated anisotropic rates. The procedure takes several iterations where ever-more accurate interpolants are used to generate ever-improving anisotropy rates. We present several numerical examples of our algorithm where the adaptive procedure successfully recovers the theoretical “best” convergence rate, including an application to a periodic PES approximation. An open-source implementation of our algorithm resides in the Tasmanian UQ library developed at Oak Ridge National Laboratory.
The pseudopotential (PP) approximation is one of the most common techniques in computational chemistry. Despite its long history, the development of custom PPs has not tracked with the explosion of different density functional approximations (DFAs). As a result, the use of PPs with exchange/correlation models for which they were not developed is widespread, although this practice is known to be theoretically unsound. The extent of PP inconsistency errors (PPIEs) associated with this practice has not been systematically explored across the types of energy differences commonly evaluated in chemical applications. Here, we evaluate PPIEs for a number of PPs and DFAs across 196 chemically relevant systems of both transition-metal and main-group elements, as represented by the W4-11, TMC34, and S22 data sets. Near the complete basis set limit, these PPs are found to cleanly approach all-electron (AE) results for noncovalent interactions but introduce root-mean-squared errors (RMSEs) upwards of 15 kcal mol –1 into predictions of covalent bond energies for a number of popular DFAs. We achieve significant improvements through the use of empirical atom- and DFA-specific PP corrections, indicating considerable systematicity of the PPIEs. The results of this work have implications for chemical modeling in both molecular contexts and for DFA design, which we discuss.
This work demonstrates how the structure of a grain boundary (GB) and its evolution due to slip transmission history influences subsequent dislocation transmission. First, a model for the evolution of stress fields within grain boundaries that accounts for the effects of coherent dislocation transmission is introduced. Starting with a disclination-based construct of GBs at minimum energy (equilibrium), the model describes the evolution of the GB stress field to a state characteristic of excess energy (non-equilibrium) due to the incorporation of residual Burgers vector content following sequential slip transmission events. Several essential features of this model are verified via molecular dynamics simulations of lattice dislocation absorption. Second, this model is implemented into a discrete dislocation dynamics (DDD) code and simulations are performed to understand the influence of Non-equilibrium GB stress fields, conditioned by the slip transmission history, on subsequent dislocation transmission. DDD simulations reveal that the critical resolved stress necessary for slip propagation can be reduced by with continued absorption of residual dislocation content. Moreover, DDD simulations prove that a comprehensive consideration of both the binding and driving stresses, and the evolution of the transmission configuration is necessary to quantify the influence of the mechanical state of the GB on slip propagation. In general, this work provides important insights into the role of GB structure evolution, conditioned by prior deformation history, on intergranular plasticity.