Radial Distribution Function Research Articles

The structure of water-ammonia mixtures from classical and abinitio molecular dynamics.

The structure of aqueous ammonia solutions is investigated through classical molecular dynamics (MD) and abinitio molecular dynamics (AIMD) simulations. We have preliminarily compared three well-known classical force fields for liquid water (SPC, SPC/E, and TIP4P) in order to identify the most accurate one in reproducing AIMD results obtained at the Generalized Gradient Approximation (GGA) and meta-GGA levels of theory. Liquid ammonia has been simulated by implementing an optimized force field recently developed by Chettiyankandy et al. [Fluid Phase Equilib. 511, 112507 (2020)]. Analysis of the radial distribution functions for different ammonia concentrations reveals that the three water force fields provide comparable estimates of the mixture structure, with the SPC/E performing slightly better. Although a fairly good agreement between MD and AIMD is observed for conditions close to the equimolarity, at lower ammonia concentrations, important discrepancies arise, with classical force fields underestimating the number and strength of H-bonds between water molecules and between water and ammonia moieties. Here, we prove that these drawbacks are rooted in a poor sampling of the configurational space spanned by the hydrogen atoms lying in the H-bonds of H2O⋯H2O and, more critically, H2O⋯NH3 neighbors due to the lack of polarization and charge transfer terms. This way, non-polarizable classical force fields underestimate the proton affinity of the nitrogen atom of ammonia in aqueous solutions, which plays a key role under realistic dilute ammonia conditions. Our results witness the need for developing more suited polarizable models that are able to take into account these effects properly.

Li2O concentration influenced local structure and properties of molten LiCl salt by machine learning driven molecular dynamics simulation

Pyroprocessing is one of the promising technologies for the reprocessing of spent nuclear fuel. In the electrolytic reduction process of pyroprocessing, LiCl serves as the electrolyte with a minor addition of Li2O as the initial oxygen ion input. Investigating the impact of Li2O concentration on the molten salt system is essential for enhancing the efficiency of electrolytic reduction. In the present study, the LiCl-Li2O system is simulated using deep potential molecular dynamics simulation. The properties including self-diffusion coefficient, shear viscosity, ionic conductivity, heat capacity, radial distribution functions, coordination numbers, short-range order, medium-range order, Voronoi structure, and angle distribution functions are explored at different Li2O concentrations. This study reveals that the variations of physical properties at different Li2O concentrations in LiCl molten salt can be attributed to the changes in local structure. For example, unnormal high proportions of two Voronoi structures are found in the molten salt when the Li2O concentration increases, which may explain the changes of properties. These findings offer valuable insights for refining the parameters of electrolytic reduction and elucidating the correlation between microscopic and macroscopic phenomena.

Ordered and disordered stealthy hyperuniform point patterns across spatial dimensions

In previous work [] it was shown that stealthy hyperuniform systems can be regarded as hard spheres in Fourier space in the sense that the structure factor is exactly zero in a spherical region around the origin in analogy with the pair-correlation function of real-space hard spheres. While this earlier work focused on spatial dimensions d=1–4, here we extend the analysis to higher dimensions in order to make connections to high-dimensional sphere packings and the mean-field theory of glasses. We exploit this correspondence to confirm that the densest Fourier-space hard-sphere system is that of a Bravais lattice in contrast to real-space hard spheres, whose densest configuration is conjectured to be disordered. In passing, we give a concise form for the position of the first Bragg peak. We also extend the virial series previously suggested for disordered stealthy hyperuniform systems to higher dimensions in order to predict spatial decorrelation as a function of dimension. This prediction is then borne out by numerical simulations of disordered stealthy hyperuniform ground states in dimensions d=2–8, which have only recently been made possible due to a highly parallelized algorithm. Published by the American Physical Society 2024

Electron diffraction structure analysis of the amorphous polytetrafluoroethylene film

A thin amorphous film of fluoroplast (polytetrafluoroethylene) was obtained and studied by the electron diffraction structure analysis methods. The previously developed method of constructing the radial distribution function of atoms by the electron diffraction patterns from amorphous structures is applied, based on the determination of the normalization coefficient by varying the thermal parameter. The possibility of applying and necessary adaptations of this technique for the studied polytetrafluoroethylene samples are shown, taking into account its not quite usual amorphous structure due to the rigid spiral conformation of polymer chains …–CF2–…, which does not allow us to speak about the complete absence of the long-range order of the arrangement of atoms along the direction of the spiral axis. The measurements were carried out using the developed registration system on the electron diffraction camera EMR-102.

A machine learning potential construction based on radial distribution function sampling.

Sampling reference data is crucial in machine learning potential (MLP) construction. Inadequate coverage of local configurations in reference data may lead to unphysical behaviors in MLP-based molecular dynamics (MLP-MD) simulations. To address this problem, this study proposes a new on-the-fly reference data sampling method called radial distribution function (RDF)-based data sampling for MLP construction. This method detects and extracts anomalous structures from the trajectories of MLP-MD simulations by focusing on the shapes of RDFs. The detected structures are added to the reference data to improve the accuracy of the MLP. This method allows us to realize a reasonable MLP construction for liquid water with minimal additional data. We prepare data from an H2O molecular cluster system and verify whether the constructed MLPs are practical for bulk water systems. MLP-MD simulations without RDF-based data sampling show unphysical behaviors, such as atomic collisions. In contrast, after applying this method, we obtain MLP-MD trajectories with features, such as RDF shapes and angle distributions, that are comparable to those of ab initio MD simulations. Our simulation results demonstrate that the RDF-based data sampling approach is useful for constructing MLPs that are robust to extrapolations from molecular cluster systems to bulk systems without any specialized know-how.

Understanding the Adsorption and Diffusion Behaviors of PBUT in Biocompatible MOFs.

With the increasing incidence of chronic kidney disease, the effective control of protein-bound uremic toxins (PBUTs), which are difficult to remove through dialysis, has become a priority. In this study, the adsorption and diffusion behaviors of several metal-organic frameworks (MOFs) for PBUTs (indoxyl sulfate and p-cresyl sulfate) were studied by molecular dynamics (MD) simulations and umbrella sampling. For the NU series of MOFs, good correlations between the Gibbs free energy (ΔG) and the experimental clearance rates of PBUTs are found. For the adsorption behaviors, in terms of ΔG, DAJWET exhibits the best adsorption effect for indoxyl sulfate (IS), whereas NU-1000 shows the best effect for p-cresyl sulfate (pCS). Similar trends observed in the radial distribution function and mean square displacement results suggest that the π-π stacking interactions play a crucial role in the adsorption and diffusion of PBUTs by MOFs. Furthermore, it can be concluded that MOFs with highly conjugated groups (porphyrin rings and pyrene groups) tend to generate more PBUT attraction, and provide design principles for potential MOF candidates in the removal of PBUTs.

Exploiting Metal-Organic Frameworks for Vinylidene Fluoride Adsorption: From Force Field Development, Computational Screening to Machine Learning.

Metal-organic frameworks (MOFs) represent a distinctive class of nanoporous materials with considerable potential across a wide range of applications. Recently, a handful of MOFs has been explored for the storage of environmentally hazardous fluorinated gases (Keasler et al. Science 2023, 381, 1455), yet the potential of over 100,000 MOFs for this specific application has not been thoroughly investigated, particularly due to the absence of an established force field. In this study, we develop an accurate force field for nonaversive hydrofluorocarbon vinylidene fluoride (VDF) and conduct high-throughput computational screening to identify top-performing MOFs with high VDF adsorption capacities. Quantitative structure-property relationships are analyzed via machine learning models on the combinations of geometric, chemical, and topological features, followed by feature importance analysis to probe the effects of these features on VDF adsorption. Finally, from detailed structural analysis via radial distribution functions and spatial densities, we elucidate the significance of different interaction modes between VDF and metal nodes in top-performing MOFs. By synergizing force-field development, computational screening, and machine learning, our findings provide microscopic insights into VDF adsorption in MOFs that will advance the development of new nanoporous materials for high-performance VDF storage or capture.

Size dependence of transport coefficients of thin and long asphaltene molecules as determined using a mode-coupling theory approach

Rotational diffusion coefficients (Dr) and viscosities (η) of rigid rod molecules consisting of N beads were first revisited for the situation at low density. Starting from this, the transport coefficients at high density condition were expressed in terms of different density pair correlation functions, such as intermolecular radial distribution function and structure factor. This was attained using a mode-coupling theory approach to approximate the intermolecular force–force correlation function, and thus the friction coefficient. As these density pair correlation functions are dependent on N, they determine how “entangled” the rigid rod molecules are, and therefore reproducing the crossover in the log –log plot of transport coefficients with N. It was found that there are crossovers in transport coefficients from Dr∼N−2.3 and η∼N2.3 at low N to Dr∼N−6.5 and η∼N6.5 at high N. The strength of N dependence is mainly determined by the competition between available free space and the compressibility of the system.

Long timescale molecular dynamics simulations of carboxylic acid-modified anatase TiO[formula omitted](101)-water interfaces using ab-initio deep neural network potentials

Carboxylic acid-modified anatase TiO2-water interfaces are widely relevant, yet understanding of their molecular scale structure is limited. To help improve this understanding, we here construct a deep neural network potential (DP) that accurately represents the potential energy surface of the formic (FA) and acetic acid (AA)-covered anatase TiO2(101) (A101) interfaces with water predicted by Density Functional Theory (DFT) with the SCAN exchange–correlation functional. Long time-scale (ns) Molecular Dynamics simulations employing such DP provide insight into the hydration structure at the interface, showing how the water density profile and radial distribution functions depend on the coverage and adsorption configurations of the acids. The developed model sets the stage for estimating the adsorption energetics of these small carboxylic acids on the A101 surface in an aqueous environment.

Quantifying Hydrogen-Bonding Interactions in the Self-Assembly of Photoresponsive Azobenzene Amphiphiles at the Air-Water Interface.

Amphiphilic azobenzene molecules offer ample scope to design functional supramolecular systems in an aqueous medium that can be controlled by light. Despite their widespread applications in photopharmacology and optoelectronics, the self-assembly pathways and energy landscapes of these systems are not well understood. Here, we report combined molecular dynamics (MD) simulation and surface manometry studies on a specially designed alkylated, meta-substituted azobenzene derivative to quantify the hydrogen-bonding interactions in the self-assembled monolayers of its photoisomers. The z-density profile, radial distribution function, order parameters, and hydrogen bond analyzed using MD simulations corroborated the experimental observations of changes in surface pressure, dipole moment, and thickness of the monolayers. Even a small change in the number of hydrogen bonds in the molecule-molecule and molecule-water interactions causes significant changes in the monolayer properties. These results are fundamentally important for engineering photoresponsive molecules with tailored properties for applications in targeted drug delivery and other industrial applications.

Study on the Factors Affecting the Self-Healing Performance of Graphene-Modified Asphalt Based on Molecular Dynamics Simulation.

To comprehensively understand the impact of various environmental factors on the self-healing process of graphene-modified asphalt, this study employs molecular dynamics simulation methods to investigate the effects of aging degree (unaged, short-term aged, long-term aged), asphalt type (base asphalt, graphene-modified asphalt), healing temperature (20 °C, 25 °C, 30 °C), and damage degree (5 Å, 10 Å, 15 Å) on the self-healing performance of asphalt. The validity of the established asphalt molecular models was verified based on four physical quantities: density, radial distribution function analysis, glass transition temperature, and cohesive energy density. The simulated healing time for the asphalt crack model was set to 200 ps. The following conclusions were drawn based on the changes in density, mean square displacement, and diffusion coefficient during the simulated healing process under different influencing factors: Dehydrogenation and oxidation of asphalt molecules during the aging process hinder molecular migration within the asphalt crack model, resulting in poorer self-healing performance. As the service life increases, the decline in the healing performance of graphene-modified asphalt is slower than that of base asphalt, indicating that graphene-modified asphalt has stronger anti-aging properties. When the vacuum layer in the asphalt crack model is small, the changes in the diffusion coefficient are less pronounced. As the crack width increases, the influence of various factors on the diffusion coefficient of the asphalt crack model becomes more significant. When the crack width is large, the self-healing effect of asphalt is more dependent on these influencing factors. Damage degree and oxidative aging have a more significant impact on the healing ability of graphene-modified asphalt than healing temperature.

Deep Learning Potential Assisted Prediction of Local Structure and Thermophysical Properties of the SrCl2-KCl-MgCl2 Melt.

The local structure and thermophysical properties of SrCl2-KCl-MgCl2 melt were revealed by deep potential molecular dynamicsdriven by machine learning to facilitate the development of molten salt electrolytic Mg-Sr alloys. The short- and intermediate-range order of the SrCl2-KCl-MgCl2 melts was explored through radial distribution functions and structure factors, respectively, and their component and temperature dependence were discussed comprehensively. In the MgCl2-rich system, the intermediate-range order is more pronounced, and its evolution with temperature exhibits a non-Debye-Waller behavior. Mg-Cl is dominated by 4,5 coordination and Sr-Cl by 6,7 coordination, and their coordination geometries exhibit distorted octahedra and distorted pentagonal bipyramids, respectively. A database of thermophysical properties of SrCl2-KCl-MgCl2 melts, including density, self-diffusion coefficient, viscosity, and ionic conductivity, was thus developed, covering the temperature range from 873 to 1173 K.

MOLECULAR DYNAMICS SIMULATION OF STRUCTURAL PROPERTIES OF Al<sub>2</sub>O<sub>3</sub>-SiO<sub>2</sub>-CaO OXIDE DURING THE COOLING PROCESS

The change of structural properties of Al2O3-SiO2-CaO oxide during the cooling process was studied using the molecular dynamic simulation. The microstructures of the sample were investigated through the radial distribution function (RDF), coordination number (CN), and bond angle (BA) distribution. The results show a structural change from the liquid state to the amorphous state when the sample is cooled from 4000 K to 300 K. The glass transition occurs at Tg = 1585 K. The fraction of AlO4, CaO6, and SiO4 units increases with decreasing temperature and dominates at 300 K.

Enhancing the thermal conductivity and viscosity of ethylene glycol-based single-walled carbon nanotube (SWCNT) nanofluid: An investigation utilizing equilibrium molecular dynamics simulation

In this work, thermal conductivity (TC), viscosity, and rheological properties of an ethylene glycol (EG) based single-walled carbon nanotube (SWCNT) nanofluid (NF) have been computed using equilibrium molecular dynamics (EMD) simulation. In SWCNT, for the interaction between carbon atoms, Tersoff potential is used. Results indicate that TC and viscosity increase in nonlinear fashion with volume fraction. However, with temperature, TC increases but viscosity decreases. Increased interaction between CNT and liquid atoms of EG, and the high heat conductance ability of SWCNT nanoparticles enhance the effective conductivity and viscosity of NFs. Longer CNTs provide more efficient heat transfer pathways and more interactions between CNT & base fluid molecules, which contribute to enhanced TC and viscosity of NFs. Weakening of intermolecular forces within the NF with increasing temperature decreases viscosity. To validate the results, radial distribution function (RDF) and stress autocorrelation function (SACF) have been estimated. Mean square displacement (MSD) investigation demonstrates that the diffusion of liquid atoms (or molecules) serves as the fundamental mechanism for heat conduction in nanofluid. The results have been compared with experimental findings for analogous dispersive medium. Broadly, an attempt has been made to explore how interactions between the base fluid and nanoparticles (NPs) can enhance the thermal and rheological efficiencies of nanofluids.

Rheology and Structure of Model Smectite Clay: Insights From Molecular Dynamics

AbstractThe low frictional strength of smectite minerals, such as montmorillonite, is thought to play a critical role in controlling the rheology and the stability of clay‐rich faults. In this study, we perform molecular dynamics simulations on a model clay system. Clay platelets are simplified as oblate ellipsoids interacting via the Gay‐Berne potential. We study the rheology and structural development during shear in this model system, which is sheared at constant strain rates for 10 strains after compression and equilibrium. We find that the system exhibits velocity‐strengthening behavior over a range of normal stresses from 1.68 to 56.18 MPa and a range of strain rates from 6.93 × 105 to 6.93 × 108/s. The relationship between shear stress and strain rate follows the Herschel‐Bulkley model. Shear localization is observed at lower strain rates despite the velocity‐strengthening friction, while homogeneous shear is realized at higher strain rates. The structure change due to shear is analyzed from various aspects: the porosity, particle orientation, velocity profile, and the parallel radial distribution function. We find that particle rearrangement and compaction dominate at the early stage of shear when the shear stress increases. The shear band starts to form in the later stage as the shear stress decreases and relaxes to a steady‐state value. The structural development at low strain rates is similar to previous experimental observations. The stacking structure is reduced during shear and restores logarithmically with time in the rest period.

Molecular Dynamics Simulation of Effect of Carbon Nanotube Diameter on Properties of Crosslinked Epichlorohydrin Rubbers.

Molecular dynamics simulation (MD) technology can be used to simulate and study the physicochemical properties of polymer materials on the basis of material data obtained in traditional experiments. In this study, we use MD to construct models of crosslinked carbon nanotubes/epichlorohydrin rubber composites with different carbon nanotube diameters and study the effect of CNT tube diameter on crosslinked ECO. The results show that with the increase in CNT tube diameter, the contact area between the CNTs and rubber matrix increases, the interaction force is enhanced, the free volume fraction of rubber matrix decreases by 21.36%, the glass transition temperature increases by 6.5%, the mean square displacement decreases, the radius of gyration decreases by 3.18 Å, and the radial distribution function of the C-atom group and the H-atom group in the system gradually decreases. The binding energy between the two is elevated, which in turn verifies the enhancement of the interaction force.

Pair correlations and freezing transitions in a two-dimensional system of tilted axially symmetric quadrupoles

We study the pair structure and the transition from the liquid to the solid state in a system of axially symmetric charged particles confined to a two-dimensional plane and characterised by only one non-zero component of the electric quadrupole moment, which we assume to be inclined with respect to the perpendicular to the confining plane. The particles interact via anisotropic repulsive quadrupole–quadrupole interactions. The pair correlation functions (PCF) were calculated by solving the hypernetted chain integral equation (HNC) theory in the range of the tilt angle 75.0 ∘ ≤ 90 ∘ − ϕ ≤ 90 ∘ . NVT Monte Carlo (MC) simulations were also performed to test the accuracy of the PCFs. We find that the PCFs calculated with the HNC theory agree well with those of the MC simulation at smaller tilt angles and show anomalous behaviour with respect to the in-plane angular variation. The PCFs obtained with the HNC theory were used in the Ramakrishnan-Yussouff (RY) density functional theory (DFT) to investigate the transition between liquid and solid freezing. It was found that the RY DFT stabilises the triangular solid only for tilt angles ≥ 80 ∘ . When the tilt angle is decreased compared to the normal orientation of the quadrupoles, the transition density increases.

Computational insights into the structural, thermodynamic and transport properties of CaF2-MgF2 binary fluoride system at high temperatures

The structural, thermodynamic and transport properties of the CaF2-MgF2 molten salt system were investigated with ab initio molecular dynamics (AIMD), system-specific neural network interatomic potentials (NNIPs) and universal PreFerred Potentials (PFP). We trained an NNIP model using AIMD data as input and used this potential to efficiently simulate the interactions within a large supercell in a temperature range of 1273–1773 K. The Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code was employed to validate our trained NNIP model. The Matlantis software with universal PFP is also presented to prove its feasibility for MD calculations and can be considered as a useful alternative simulation tool for higher-order systems where existing potentials are not readily available. We calculated structural and thermodynamic properties including radial distribution function (RDF), angular distribution function (ADF), specific heat capacity, ionic self-diffusivity, and viscosity. Our results indicate that the system exhibited a high degree of structural disorder, with the Ca, Mg, and F ions forming a liquid solution. Using PFP, the positions of the first peak in RDFs for Ca-F and Mg-F pairs are only slightly left-shifted (<0.05 Å), and the estimated viscosity of the melt decreases from 4.613 mPa·s to 1.846 mPa·s with an increase in temperature from 1273 K to 1773 K, in agreement with the NNIP trained specifically for CaF2-MgF2. Our results provide valuable insights into the properties of the CaF2-MgF2 system at high temperatures and serve as predictive models for the development of new electrolytes that could be used for silicon epitaxy by adding silica.

Molecular dynamics simulation on the variations in characteristics of aqueous solution with diverse additives: Inspirations for binary ice preparation

Ice slurry, as a novel energy storage medium, possesses the superiority of high energy storage density. The freezing point inhibitors are employed to regulate the supercooling phenomenon of water, while the macroscopic experiments are complicated to elucidate the action mechanism. Consequently, this paper adopts molecular dynamic simulation method to investigate the mean square displacement, radial distribution function, hydrogen bonds types, hydrogen bonds number and length of water at various concentrations of ethanol, NaCl, and SiO2 additives. The results indicate that the average number of hydrogen bonds of the system composed with 600 pure water molecules is 1127.24, and the average length of hydrogen bonds is 2.33 Å. Moreover, the addition of alcoholics and chlorides inhibits the hydrogen bonds between water and water molecules, and promotes the generation of hydrogen bonds, thus weakening the diffusion motion of water molecules. Whereas, it is observed that the addition of nanoparticles leads to agglomeration in pure water system, resulting an increase in the viscosity and a decrease in the diffusion rate of the system. The proposed microscopic model can effectively depict the potential interaction mechanisms of additives and water, which is contributing to develop effective freezing point inhibitors and improve the energy utilization efficiency.

High adsorption capacities for dyes by a pH-responsive sodium alginate/sodium lignosulfonate/carboxylated chitosan/polyethyleneimine adsorbent

Dyes are indispensable for the rapid development of society, but untreated dye wastewater can threaten human health. In this study, an adsorbent (SA/SL/CCS/PEI@MNPs) was synthesized by one-pot method using magnetic nanoparticles (MNPs), sodium alginate (SA), sodium lignosulfonate (SL), carboxylated chitosan (CCS) and polyethyleneimine (PEI). The adsorbent was mesoporous micrometer-sized particles with pore size of 34.92 nm, which was favorable for dynamic column experiments. SA/SL/CCS/PEI@MNPs possessed pH-responsive performance. Under acidic condition, the maximum adsorption capacities for anionic dyes (tartrazine, reactive black-5, indigo carmine) reached >550 mg/g. Under alkaline condition, those for cationic dyes (methylene blue, methyl violet, neutral red) exceeded 1900 mg/g. The function of the various modifiers was investigated. The results indicated that the incorporation of SL, CCS and PEI was able to provide plenty of sulfonate, carboxylate and amino/imine reactive groups so that adsorption capacities of dyes were improved. The adsorption mechanism was explored by FTIR and XPS. At the same time, the adsorption mechanism was more deeply analyzed using molecular dynamics simulations and radial distribution function. It was demonstrated that the dyes adsorption on the SA/SL/CCS/PEI@MNPs was mainly due to electrostatic attraction and π-π interaction. In addition, the adsorbent had good reusability, and the removal still reached over 90 % after five cycles. In conclusion, the adsorbent displayed a broad prospect for the adsorption of organic dyes.