Radial Distribution Function Research Articles

Pair correlation microscopy of intracellular molecular transport.

Pair correlation microscopy is a unique approach to fluorescence correlation spectroscopy that can track the long-range diffusive route of a population of fluorescent molecules in live cells with respect to intracellular architecture. This method is based on the use of a pair correlation function (pCF) that, through spatiotemporal comparison of fluctuations in fluorescence intensity recorded throughout a microscope data acquisition, enables changes in a molecule's arrival time to be spatially mapped and statistically quantified. In this protocol, we present guidelines for the measurement and analysis of line scan pair correlation microscopy data acquired on a confocal laser scanning microscope (CLSM), which will enable users to extract a fluorescent molecule's transport pattern throughout a living cell, and then quantify the molecular accessibility of intracellular barriers encountered or the mode of diffusion governing a molecular trafficking event. Finally, we demonstrate how this protocol can be extended to a two-channel line scan acquisition that, when coupled with a cross pCF calculation, enables a fluorescent molecule's transport pattern to be selectively tracked as a function of complex formation with a spectrally distinct fluorescent ligand. For a skilled user of a CLSM, the line scan data acquisition and analysis described in this protocol will take ~1-2 d, depending on the sample and the number of experiments to be processed.

Toxicological interactions of cosmetic and personal care additives mixtures: An update based on measurement and simulation.

The toxicity of chemical mixtures may be misestimated, as the assessment of individual chemicals may not adequately reflect their combined toxic effects. However, numerous combinations of chemicals and various interactions make it impossible to measure all possible mixtures. Computational toxicology can help to mitigate this issue, particularly with new methodologies that rely upon alternatives to animal testing. For cosmetic and personal care additives (CPCAs), the ever-increasing of consumption has triggered their complex co-existence in the aquatic environment. To assess their ecological risks, CPCAs experimentally mix at realistic low concentrations with multi-components and different combinations needs to be examined firstly. In this study, toxicity and interactions of multi-component CPCAs mixtures were analyzed taking Daphnia magna as model organism. Also, the contributions of components to the mixture toxicity at different effect levels were discussed. Apparently, the mixture toxicity is closely related to components proportion and impacted by dilution effect. Different forms of combined toxic effects occur in different effect levels. The more components, the less interactions, and the combined toxic effect tends to be additive. Then, Quantitative Structure-Activity Relationship (QSAR) models were developed and evaluated to predict the aquatic toxicity of CPCAs mixtures at various effect levels. The model performance at the median effect level is the best. The descriptors associated most to the toxicity response of CPCA multi-component mixtures are autocorrelation and radial distribution function (RDF), which provide structural information about the spatial distribution of electronic properties and atomic mass.

Aggregation Behavior of Asphaltenes in Heavy Oil and Its Influencing Factors: A Multiscale Study Based on Molecular Dynamics Simulations and Quantum Chemical Calculations

Summary Asphaltenes and resins are important and complex components of heavy oils, and their self-aggregation behavior has a profound effect on the oil and gas industry. In this study, based on three classical molecular models of asphaltenes, the aggregation laws of asphaltenes with different structures and their influencing factors were investigated using molecular dynamics (MD) simulations and quantum chemical calculations. By analyzing the equilibrium conformations, interaction energy, radial distribution functions (RDFs), mean square displacements (MSDs), diffusion coefficients, cluster analyses, radii of gyration, electrostatic potentials, and nonbonding interactions, we found that archipelago-type asphaltenes have the strongest interactions, the highest probability of occurrence, and the best stability. In contrast, continental asphaltenes have the strongest diffusion ability in the heavy oil model. Quantum chemical calculations show that the asphaltene association is mainly driven by van der Waals forces initiated by the aromatic core and electrostatic attraction around the heteroatoms, whereas the aggregation behavior is influenced by a variety of factors, such as intermolecular van der Waals forces, hydrogen bonding, and π-π interactions. In addition, external conditions, such as temperature and pressure, considerably affect the aggregation behavior of asphaltenes. This study provides a theoretical basis for exploring the viscosity mechanism of heavy oils and scientific support for the efficient development of oil and gas fields.

Methane Clathrate Inhibition by Molecular Dynamics Simulation: Which is more Effective, Ionic Liquid, Electric Field, or Magnetic Field?

Methane gas hydrates present both opportunities and threats for the oil and gas industry as well as the environment. Different inhibitors are utilized to minimize the risk of gas hydrates, making it essential to comprehend their effectiveness. In this study, inhibition of methane hydrates formed between different sheets of graphene and graphene oxides (C8O-flat, C8O-polar, and C8(OH)2) in presence of various inhibiting agents such as ionic liquid 1-ethyl-3-methylimidazolium chloride, electric field (from 101 to 104 (V/m) in X, Y, and Z directions), and magnetic field with two strong and weak intensities (10 and 100 (T)) were investigated by molecular dynamics (MD) simulation. The goal of this research is to provide a new microscopic insight into hydrate inhibitors. This will enable effective investigation and comparison of these factors. Various parameters were investigated to confirm the inhibition of methane gas hydrate, including radial distribution function (RDF), hydrogen bonds (HBs), diffusion coefficient, angular distribution function (ADF), and four-body structural order parameter. Analysis of the obtained results showed that the addition of ionic liquid and electric field effectively destroys the hydrate structure and releases methane molecules. The performance of the electric field in hydrate destruction is different for these systems. In the graphene, C8O-polar, and C8(OH)2 systems, the electric field in order of 103 (V/m) had the greatest effect. However, in the C8O-flat system, the weaker electric field in order of 102 (V/m) performed better. The role of field orientation is also different in the process of hydrate decomposition; better decomposition of hydrate nuclei occurs in the Z direction (perpendicular to the surfaces) in graphene, C8O-flat, and C8O-polar systems and in the X direction (parallel to the surfaces) in C8(OH)2 system. The increase in the number of HBs and the diffusion coefficient, along with the decrease in the four-body structural order, showed that both the ionic liquid and the electric field were effective in hydrate destruction, with the electric field showing better performance. However, the investigation of the magnetic field showed that it is not effective in destroying the hydrate structure.

Synthesis characterisation and analysis of the corrosion resistance of a new epoxy resin onto mild steel in the presence of hydrochloric acid: practical and theoretical points of view

ABSTRACT In this work, we synthetised the tetraglycidyl ether L-(+) ribose (TGER). TGER was identified through nuclear magnetic resonance spectroscopy (NMR). The effectiveness of a novel epoxy resin derived from TGER in protecting mild steel from corrosion in hydrochloric acid has been evaluated. After 30 minutes of immersion, electrochemical impedance spectroscopy revealed that TGER could reduce mild steel corrosion, achieving an inhibition rate of 93% at 10−3 M. According to the Langmuir isotherm model, chemical bonding allows the compound to adhere to the mild steel surface. Molecular structure analysis using density functional theory (DFT) supports the observed inhibition efficiency. Furthermore, molecular dynamics studies indicated that the interaction energy of the TGER inhibitor increased, while radial distribution function analysis confirmed the chemical adsorption of the inhibitors onto the metal surface. Additionally, thermodynamic activation parameters suggested that the adsorption process on the metal surface was spontaneous. The synthesised TGER epoxy resin provided maximal protection of 93% at 10−3 M, and the epoxy functional groups improved the anti-polarisation efficacy of the molecule.

Li-Ion Mobility and Solvation Structures in Concentrated Poly(ethylene carbonate) Electrolytes: A Molecular Dynamics Simulation Study

With the rapid global increase in the use of digital devices and electric vehicles, solid polymer electrolytes (SPEs) have emerged as promising candidates for all-solid-state batteries. They are expected to resolve safety concerns and overcome the limitations of energy density and charging speed associated with traditional Li-ion batteries with liquid electrolytes. However, a limited understanding of ionic conduction mechanisms remains a significant barrier to their further development and practical application. In this study, we employed molecular dynamics simulations using the COMPASS II force field under NPT/NVT ensembles at 298 K to investigate the static and dynamic properties of poly(ethylene carbonate) (PEC) electrolytes at various salt concentrations. Key analyses included the radial distribution function, solvation free energy, and mean-square displacement (MSD) of individual Li cations. Based on their MSD data, Li cations were categorized into “faster” or “slower” groups, corresponding to conductivity levels above or below the average in each model. Our findings reveal that, at higher concentrations, a smaller fraction of faster Li cations contributes disproportionately more than slower Li cations to the overall mobility, highlighting that targeted manipulation of solvation structures could enhance ion transport efficiency in highly concentrated SPEs. Additionally, changes in coordination number and solvation free energy for both faster and slower Li cations suggest the existence of three different solvation patterns as salt concentration increases. These insights provide a deeper understanding of ionic transport and solvation structures in PEC electrolytes, with potential implications for the design of more efficient all-solid-state batteries.

Finite-size effects of the excess entropy computed from integrating the radial distribution function

Computation of the excess entropy S ex from the second-order density expansion of the entropy holds strictly for infinite systems in the limit of small densities. For the reliable and efficient computation of S ex it is important to understand finite-size effects. Here, expressions to compute S ex and Kirkwood–Buff (KB) integrals by integrating the Radial Distribution Function (RDF) in a finite volume are derived, from which S ex and KB integrals in the thermodynamic limit are obtained. The scaling of these integrals with system size is studied. We show that the integrals of S ex converge faster than KB integrals. We compute S ex from Monte Carlo simulations using the Wang–Ramírez–Dobnikar–Frenkel pair interaction potential by thermodynamic integration and by integration of the RDF. We show that S ex computed by integrating the RDF is identical to that of S ex computed from thermodynamic integration at low densities, provided the RDF is extrapolated to the thermodynamic limit. At higher densities, differences up to 20 % are observed.

Neural Density Functional Theory of Liquid-Gas Phase Coexistence

We use supervised machine learning together with the concepts of classical density functional theory to investigate the effects of interparticle attraction on the pair structure, thermodynamics, bulk liquid-gas coexistence, and associated interfacial phenomena in many-body systems. Local learning of the one-body direct correlation functional is based on Monte Carlo simulations of inhomogeneous systems with randomized thermodynamic conditions, randomized planar shapes of the external potential, and randomized box sizes. Focusing on the prototypical Lennard-Jones system, we test predictions of the resulting neural attractive density functional across a broad spectrum of physical behavior associated with liquid-gas phase coexistence in bulk and at interfaces. We analyze the bulk radial distribution function g(r) obtained from automatic differentiation and the Ornstein-Zernike route and determine (i) the Fisher-Widom line, i.e., the crossover of the asymptotic (large distance) decay of g(r) from monotonic to oscillatory, (ii) the (Widom) line of maximal correlation length, (iii) the line of maximal isothermal compressibility, and (iv) the spinodal by calculating the poles of the structure factor in the complex plane. The bulk binodal and the density profile of the free liquid-gas interface are obtained from density functional minimization and the corresponding surface tension from functional line integration. We also show that the neural functional describes accurately the phenomena of drying at a hard wall and of capillary evaporation for a liquid confined in a slit pore. Our neural framework yields results that improve significantly upon standard mean-field treatments of interparticle attraction. Comparison with independent simulation results demonstrates a consistent picture of phase separation even when restricting the training to supercritical states only. We argue that phase coexistence and its associated signatures can be discovered as emerging phenomena via functional mappings and educated extrapolation. Published by the American Physical Society 2025

A coarse-grained molecular dynamics simulation on the mechanical and thermal properties of natural rubber composites reinforced by fullerene nanoparticles.

The influence of fullerene C60 on the mechanical and thermal properties of natural rubber was systematically investigated using coarse-grained molecular dynamics simulations. The tensile results demonstrate that systems with longer NR chains exhibit reduced tensile strength. Moreover, the addition of C60 nanoparticles significantly enhanced the mechanical properties, with Young's modulus, yield strength, and tensile strength increasing by approximately 24.03%, 23.21%, and 51.61%, respectively, at a C60 concentration of 0.2 phr under a strain rate of 1e-6. Furthermore, the mechanical response was found to be strain rate-dependent, with higher strain rates leading to increased Young's modulus, yield strength, and tensile strength. Therefore, excessively high strain rates should be avoided in simulations to ensure consistency with real conditions. In terms of thermal properties, the addition of C60 nanoparticles was shown to significantly improve the thermal conductivity of natural rubber, with the optimal enhancement of 17.17% achieved at an inclusion level of approximately 0.1 phr. A comprehensive microstructural analysis, including mean square displacement, radial distribution function, coordination number, and interaction energy, revealed the reinforcement mechanisms of C60 on the mechanical and thermal properties of the nanocomposites. This study provides valuable insights into the rational design and fabrication of fullerene-reinforced natural rubber nanocomposites with superior mechanical and thermal performance. In this study, coarse-grained molecular dynamics simulations were performed using the LAMMPS software. The system used the real unit system with periodic boundary conditions. The interatomic interactions were described using the lj/expand model. The simulations were conducted at a temperature of 300K with a time step of 1fs.

The orientational structure of a model patchy particle fluid: Simulations, integral equations, density functional theory, and machine learning.

We investigate the orientational properties of a homogeneous and inhomogeneous tetrahedral four-patch fluid (Bol-Kern-Frenkel model). Using integral equations, either (i) HNC or (ii) a modified HNC scheme with a simulation input, the full orientational dependence of pair and direct correlation functions is determined. Density functionals for the inhomogeneous problem are constructed via two different methods. The first, molecular density functional theory, utilizes the full direct correlation function and an isotropic hard-sphere bridge functional. The second method, a machine learning approach, uses a decomposition of the functional into an isotropic reference part and a mean-field orientational part, where both parts are improved by machine learning techniques. A comparison with the simulation data at hard walls and around hard tracers shows a similar performance of the two functionals. Machine learning strategies are discussed to eliminate residual differences, with the goal of obtaining machine-learning enhanced functionals for the general anisotropic fluid.

On the Density–Density Correlations of the Non‐Interacting Finite Temperature Electron Gas

ABSTRACTThe density–density correlations of the non‐interacting finite temperature electron gas are discussed in detail. Starting from the ideal linear density response function and utilizing general relations from linear response theory, known and novel expressions are derived for the pair correlation function, static structure factor, dynamic structure factor, thermal structure factor, and imaginary time correlation function. Applications of these expressions in the classical mapping approach, self‐consistent dielectric formalism and equation‐of‐state construction are analyzed in depth.

Biodegradable Tenebrio molitor antifreeze protein modified kinetic hydrate inhibitor: Insights into molecular interactions and structural flexibility.

The formation of natural gas hydrates presents significant economic and safety challenges to the petroleum and gas industry, necessitating the development of effective prevention strategies. This study investigates an environmentally sustainable Tenebrio molitor antifreeze protein (TmAFP) modified to be a potential kinetic hydrate inhibitor. The aim of this study was to enhance the inhibitory activity of TmAFP by systematically substituting threonine (Thr) residues with glycine (Gly), alanine (Ala), or serine (Ser) at positions 29, 39, and 53. The Ala mutant demonstrated superior inhibition of hydrate formation, attributed to its optimized spatial conformation and enhanced hydrophobic interactions, followed by the Gly and Ser mutants. The wild-type TmAFP showed limited efficacy. The radial distribution function (RDF) analysis indicated that the mutations facilitated a better accommodation of adjacent residues within the hydrate crystal structure by adjusting the distance between Thri and Thri+2 to closely match the second peak in the RDF of methane molecules at 6.4Å. The potential of mean force (PMF) calculations revealed that the Ala and Ser mutants exhibited enhanced interactions with hydrate cages, with PMF values of -0.73 and -0.71kJ/mol, respectively, compared to the Gly mutant, which had a PMF value of 1.46kJ/mol. By identifying the optimal mutation combination (T29 39 53A) to significantly increase the potency of TmAFP, this study provides a fundamental basis for the further development of hydrate inhibition strategies.

Shape-Dependent Structural Order of Red Blood Cells.

In this work, we show how shape matters for the ordering of red blood cells (RBCs) at a water-air interface for both artificially rigidified and sphered cells as a model system for hereditary spherocytosis. We report enhanced long-range order for spherical RBCs over disk-shaped RBCs arising from the increased local ordering of spheres relative to disks. We show that rigidity has a greater effect on the radial distribution of spherical vs disk-shaped RBCs by slightly increasing the average distance between cells. The onset of local hexatic bond order of spherical RBCs in mixed disc-sphere systems coincides with the appearance of clustering of spherical cells as the number fraction of spherocytes increases. Additionally, the radial distribution function in mixed-shape systems begins to change with the onset of local hexatic order and clustering of spherical RBCs. By analyzing the radial distribution functions of RBCs, local hexatic bond order, and clustering, we show that the structure of settled RBCs is dictated by shape. These shape-dictated structures may provide a basis for future tools for detecting RBC shape-altering diseases and disorders.

Unravelling Lithium Interactions in Non-Flammable Gel Polymer Electrolytes: A Density Functional Theory and Molecular Dynamics Study

Lithium metal batteries (LiMBs) have emerged as extremely viable options for next-generation energy storage owing to their elevated energy density and improved theoretical specific capacity relative to traditional lithium batteries. However, safety concerns, such as the flammability of organic liquid electrolytes, have limited their extensive application. In the present study, we utilize molecular dynamics and Density Functional Theory based simulations to investigate the Li interactions in gel polymer electrolytes (GPEs), composed of a 3D cross-linked polymer matrix combined with two different non-flammable electrolytes: 1 M lithium hexafluorophosphate (LiPF6) in ethylene carbonate (EC)/dimethyl carbonate (DMC) and 1 M lithium bis(fluorosulfonyl)imide (LiFSI) in trimethyl phosphate (TMP) solvents. The findings derived from radial distribution functions, coordination numbers, and interaction energy calculations indicate that Li⁺ exhibits an affinity with solvent molecules and counter-anions over the functional groups on the polymer matrix, highlighting the preeminent influence of electrolyte components in Li⁺ solvation and transport. Furthermore, the second electrolyte demonstrated enhanced binding energies, implying greater ionic stability and conductivity relative to the first system. These findings offer insights into the Li+ transport mechanism at the molecular scale in the GPE by suggesting that lithium-ion transport does not occur by hopping between polymer functional groups but by diffusion into the solvent/counter anion system. The information provided in the work allows for the improvement of the design of electrolytes in LiMBs to augment both safety and efficiency.

Structure factor line shape model gives approximate nanoscale size of polar aggregates in pyrrolidinium-based ionic liquids.

Room temperature ionic liquids (RTILs) are interesting due to their myriad uses in fields such as catalysis and electrochemistry. Their properties are intimately related to their structures, yet structural understanding is difficult to achieve. This work presents a derivation of an approximate expression for the radial distribution function, g(r). The derivation assumes a Lorentzian line shape for total structure factors, S(Q). The derived form for g(r) is used to present new equations for maxima and minima in g(r) and to define a half-length, the value of r where g(r) decays to one-half its maximum value. A detailed test of the model is presented using experimentally measured and calculated S(Q) for N-methyl-N-propylpyrrolidinium bis(trifluoromethylsulfonyl)imide (C1C3pyrrTFSI). Then, the model is extended to the series of RTILs with anions of increasing size, C1C3pyrrX (X- = Cl-, Br-, BF4-, PF6-, OTf-, TFSI-). The model predicts maxima and minima in the entire series within 4.2% of those calculated directly from molecular dynamics simulations. This reinforces our previous conclusion that distances within "polar scattering domains" responsible for the charge alternation peak in S(Q) are quantitatively related to inter-ionic distances within polar aggregates in these RTILs. The half-length is found to increase approximately linearly as anion size increases. We argue that the half-length is a measure of polar aggregate size in these RTILs.

Exploring the Thermodynamics and Dynamics of CO2 Using Rigid Models

Understanding the behavior of carbon dioxide (CO2) under varying thermodynamic conditions is essential for optimizing processes such as Carbon Capture and Storage (CCS) and supercritical fluid extraction. This study employs molecular dynamics (MD) simulations with the EPM2 and TraPPE-small force fields to examine CO2 phase behavior, structural characteristics, and transport properties across a temperature range of 228–500 K and pressures from 1 to 150 atm. Our findings indicate a good agreement between simulated and experimental liquid–vapor coexistence curves, validating the capability of both force fields to model CO2 accurately in a wide range of thermodynamical conditions. Radial distribution functions (RDFs) reveal distinct interaction patterns in liquid and supercritical phases, while mean squared displacement (MSD) analyses show diffusivity increasing from 5.2×10−9 m2/s at 300 K to 1.8×10−8 m2/s at 500 K. Additionally, response functions such as the heat capacity effectively capture phase transitions. These findings provide quantitative insights into CO2 phase behavior and transport properties, enhancing the predictive reliability of simulations for CCS and related industrial technologies. This work bridges gaps in the CO2 modeling literature and highlights the potential of MD simulations in advancing sustainable applications.

Validating Structural Predictions of Conjugated Macromolecules in Espaloma-Enabled Reproducible Workflows.

We incorporated Espaloma forcefield parameterization into MoSDeF tools for performing molecular dynamics simulations of organic molecules with HOOMD-Blue. We compared equilibrium morphologies predicted for perylene and poly-3-hexylthiophene (P3HT) with the ESP-UA forcefield in the present work against prior work using the OPLS-UA forcefield. We found that, after resolving the chemical ambiguities in molecular topologies, ESP-UA is similar to GAFF. We observed the clustering/melting phase behavior to be similar between ESP-UA and OPLS-UA, but the base energy unit of OPLS-UA was found to better connect to experimentally measured transition temperatures. Short-range ordering measured by radial distribution functions was found to be essentially identical between the two forcefields, and the long-range ordering measured by grazing incidence X-ray scattering was qualitatively similar, with ESP-UA matching experiments better than OPLS-UA. We concluded that Espaloma offers promise in the automated screening of molecules that are from more complex chemical spaces.

Structure and dynamics of piperidinium based amino acid ionic liquids: a computational investigation.

There has been growing interest in amino acid ionic liquids because of their low-cost synthesis and superior biodegradability and biocompatibility compared to traditional ionic liquids. In this study, we have investigated the structure and dynamics of three ionic liquids consisting of N-butyl N-methyl piperidinium[Pip] cation with amino acid (lysine [Lys], histidine [His], and arginine [Arg]) anions. The radial distribution functions, the spatial distribution functions, and the coordination numbers have been used to analyze the structure in the bulk phase. The time-correlation functions for hydrogen bonds, ion pairs, and ion cage formation have been calculated to analyze the dynamic properties. The hydrogen bonds found between the ion pairs are mostly electrostatically dominant with moderate to weaker strengths. The [Pip][His] system showed the strongest interaction energy between the ion pairs, while the [Pip][Lys] system demonstrated faster dynamics consistent with its higher diffusion and ion conductivity. The density functional theory at M06-2X/6-311 + + G(d,p) level was employed for geometry optimization and wave function calculations. The theory of atoms-in-molecule was used for the topological analysis of electron density. The classical molecular dynamics simulations with OPLS-AA force field were employed to study the dynamics of the systems.

Elucidating the Oxidation Process and Enhanced Stability of Black Phosphorus through NTCDA Passivation: A Molecular Dynamics Study

AbstractUnderstanding the oxidation mechanisms of black phosphorus (BP) at the atomic scale is essential for developing effective passivation strategies to enhance its stability in ambient conditions. To explore this, the effects of O2 and H2O molecules on BP layers are elucidated using reactive force field (ReaxFF) molecular dynamics simulations at constant concentrations of molecules and room temperature. As a potential solution, the passivation efficacy of 1,4,5,8‐naphthalenetetracarboxylic dianhydride (NTCDA) is evaluated. The initial oxidation processes are analyzed through atomic structural changes, charge dynamics, and radial distribution functions. Moreover, the thickness of the oxidized BP layers is quantitatively determined. Results show that elevated O2 concentrations significantly accelerate oxidation and increase the thickness of the oxidized layers, while H2O has a weaker influence. The interaction between O⁻ and H⁺ ions in H2O reduces its interaction with BP, but O2 molecules cause H2O to become negatively charged, allowing it to interact with P⁺ ions. Importantly, passivating BP with NTCDA effectively mitigates oxidation, creating a protective layer that repels O2 molecules. Ultimately, this study reveals the initial oxidation and passivation processes of BP layers, offering crucial theoretical insights to guide experimental methods and practical applications in semiconductor devices.

Theoretical and computational study on structure, dynamics, and configurational entropy correlation in gold-silicon liquid

The structure–property relationship of partially ordered systems poses a different type of open problem for both theoretical and experimental condensed matter researchers. Configurational entropy is an important thermodynamic property that characterizes the glass transition ability of binary liquid alloys. Recently, various experimental and computational approaches have been reported to investigate the configurational entropy in liquids; however, a well-established theoretical definition is still lacking. In this study, the configurational entropy of binary melts has been computed using their pair correlation functions. We determine three partial structure factors that govern the total structure factor S(k) in liquid AuySix alloys at different compositions and temperatures. Fourier inversion of partial and total structure factors gives partial pair correlation functions and radial distribution functions g(r) of AuySix melts, respectively. The computed values of S(k) and g(r) are in excellent agreement with available experimental results. The present model calculation of S(k) for eutectic AuySix melts (x = 19 at. % Si) shows better agreement with the experimental values than the molecular dynamics simulation data. Furthermore, we determine the friction coefficients experienced by constituent particles in the attractive and repulsive regions of the square-well (SW) potential function and employed in Einstein's equation to determine the self- and mutual diffusion coefficients as a function of composition and temperature. The diffusivity of Au and the mutual diffusion coefficient of the alloy are also in good agreement with experimental values compared to molecular dynamics data at its eutectic composition, which confirms the applicability of the SW model for such alloys.