Number Of Molecular Dynamics Simulations Research Articles

A data-driven multiscale model for reactive wetting simulations

We describe a data-driven, multiscale technique to model reactive wetting of a silver–aluminum alloy on a Kovar™ (Fe-Ni-Co alloy) surface. We employ molecular dynamics simulations to elucidate the dependence of surface tension and wetting angle on the drop’s composition and temperature. A design of computational experiments is used to efficiently generate training data of surface tension and wetting angle from a limited number of molecular dynamics simulations. The simulation results are used to parameterize models of the material’s wetting properties and compute the uncertainty in the models due to limited data. The data-driven models are incorporated into an engineering-scale (continuum) model of a silver–aluminum sessile drop on a Kovar™ substrate. Model predictions of the wetting angle are compared with experiments of pure silver spreading on Kovar™ to quantify the model-form errors introduced by the limited training data versus the simplifications inherent in the molecular dynamics simulations. The paper presents innovations in the determination of “convergence” of noisy MD simulations before they are used to extract the wetting angle and surface tension, and the construction of their models which approximate physio-chemical processes that are left unresolved by the engineering-scale model. Together, these constitute a multiscale approach that integrates molecular-scale information into continuum scale models.

Prediction of nonlocal elasticity parameters using high-throughput molecular dynamics simulations and machine learning

Higher-order elasticity theories are able to model the mechanics at nanoscale. However, these theories have length-scale parameters that need to be evaluated either through experiments or molecular dynamics (MD) simulations. In the current literature, these length-scale parameters are assumed to be constant for a chosen material. However, this is not true, as shown in this paper through a set of high-throughput MD simulations on a variety of boundary value problems (BVP). For carbon and boron nitride nanotubes, the length-scale parameter in the modified strain gradient theory is found to vary with the absolute dimensions, type of the BVP, and the deformation level. This complex dependence makes prediction of the length-scale parameter l0 challenging. To address this issue, a supervised machine learning (ML) based framework is developed here. In this new framework, MD, continuum formulation, and ML are used in tandem to predict the length-scale parameter for a given material, dimension, and boundary condition. It is a three-step process. First, a number of MD simulations are performed for varying chirality, length, and boundary conditions. Then, the values of l0 are found using these MD output in the continuum formulation. Finally, an ML-based regression model is used to capture the variability of l0 and subsequently predict its value for any chirality, length, and boundary condition. Here three regression models – Gaussian process regression, support vector machine, and neural network – are used to make the predictions.This predictive tool eliminates need for expensive MD simulations for further continuum scale analysis. In a broader context, this novel three-step framework opens an accurate yet inexpensive door for applying non-classical continuum theories to nanoscale mechanics problems. The accuracy is imposed by the MD simulations, while the computational cost reduction is achieved by machine learning.

Concentration-temperature superposition principle for grain boundary migration in Ni(Cu) bicrystals

It is known that both the temperature and alloying element concentration have considerable effects on the GB migration in alloys. Analogous to the time-temperature superposition in viscoelasticity, the concentration-temperature superposition principle is presented for the GB migration in alloys. The GB migration displacement increased by elevating temperature can be achieved by reducing the alloying element concentration. A large number of molecular dynamics simulations are performed on Ni(Cu) bicrystal. The GB migration displacement is inversely proportional to the solute concentration and exponentially related to the temperature. The main curves at the reference temperature for different loading strains are constructed through a temperature-related shift factor. Finally, the validation is conducted by a comparison between its predictions and the simulation results.

High-temperature ion-thermal behavior from average-atom calculations.

Atom-in-jellium calculations of the Einstein frequency were used to calculate the mean displacement of an ion over a wide range of compression and temperature. Expressed as a fraction of the Wigner-Seitz radius, the displacement is a measure of the asymptotic freedom of the ion at high temperature, and thus of the change in heat capacity from six to three quadratic degrees of freedom per atom. A functional form for free energy was proposed based on the Maxwell-Boltzmann distribution as a correction to the Debye free energy, with a single free parameter representing the effective density of potential modes to be saturated. This parameter was investigated using molecular dynamics simulations, and found to be ∼0.2 per atom. In this way, the ion-thermal contribution can be calculated for a wide-range equation of state (EOS) without requiring a large number of molecular dynamics simulations. Example calculations were performed for carbon, including the sensitivity of key EOS loci to ionic freedom.

Bundling in Molecular Dynamics Simulations To Improve Generalization Performance in High-Dimensional Neural Network Potentials

We examined the influence of using bundling trajectories in molecular dynamics (MD) simulations for predicting energies in high-dimensional neural network potentials. In particular, we focused on the chemical transferability of gold nanoclusters, that is, how well the energy of gold clusters was estimated from the training data comprising gold clusters of different shapes and sizes. We observed that as the number of MD simulations in the training data increased, the accuracy of the predicted energies improved.

A closed-form criterion for dislocation emission in nano-porous materials under arbitrary thermomechanical loading

Our traditional view of void nucleation is associated with interface debonding at second-phase particles. However, under extreme dynamic loading conditions second-phase particles may not necessarily be the dominant source of void nucleation sites. A few key experimental observations of laser spall surfaces support this assertion. Here, we describe an alternative mechanism to the traditional view, namely shock-induced vacancy generation and clustering followed by nanovoid growth mediated by dislocation emission. This mechanism only becomes active at very large stresses. It is therefore desirable to establish a closed-form criterion for the macroscopic stress required to activate dislocation emission in porous materials. Following an approach similar to Lubarda and co-workers, we derive the desired criterion by making use of stability arguments applied to the analytic solutions for the elastic interactions of dislocations and voids. Our analysis significantly extends that of Lubarda and co-workers by accounting for a more general stress state, finite porosity, surface tension, as well as temperature and pressure dependence. The resulting simple stress-based criterion is validated against a number of molecular dynamics simulations with favorable agreement.

Modeling the effect of codon translation rates on co-translational protein folding mechanisms of arbitrary complexity.

In a cell, the folding of a protein molecule into tertiary structure can begin while it is synthesized by the ribosome. The rate at which individual amino acids are incorporated into the elongating nascent chain has been shown to affect the likelihood that proteins will populate their folded state, indicating that co-translational protein folding is a far from equilibrium process. Developing a theoretical framework to accurately describe this process is, therefore, crucial for advancing our understanding of how proteins acquire their functional conformation in living cells. Current state-of-the-art computational approaches, such as molecular dynamics simulations, are very demanding in terms of the required computer resources, making the simulation of co-translational protein folding difficult. Here, we overcome this limitation by introducing an efficient approach that predicts the effects that variable codon translation rates have on co-translational folding pathways. Our approach is based on Markov chains. By using as an input a relatively small number of molecular dynamics simulations, it allows for the computation of the probability that a nascent protein is in any state as a function of the translation rate of individual codons along a mRNA's open reading frame. Due to its computational efficiency and favorable scalability with the complexity of the folding mechanism, this approach could enable proteome-wide computational studies of the influence of translation dynamics on co-translational folding.

Thermodynamic States of Nanoclusters at Low Pressure and Low Temperature: The Case of 13 H2

A confinement model of finite-size systems that embodies an equation of state is presented. The temperature and pressure of the system are obtained from the positions and velocities of the enclosed particles after a number of molecular dynamics simulations. The pressure has static and dynamic (thermal) contributions, extending the Mie-Grüneisen equation of state to include weakly interacting anharmonic oscillators. The model is applied to a system of 13 H(2) molecules under low-pressure and low-temperature conditions in the classical regime. The confining cage in this case is a spherical hydrogen cavity. The Born-Oppenheimer molecular dynamics in conjunction with density functional theory are used for the time evolution of the particle system. The hydrogen molecules form a noncrystalline cluster structure with icosahedral symmetry that remains so in the whole temperature range investigated. The fluctuations of the interatomic distances increase with the temperature, while the orientational order of the enclosed system of molecules fades out, suggesting a gradual order-disorder transition.

Characterization of the outer membrane protein OprF of Pseudomonas aeruginosa in a lipopolysaccharide membrane by computer simulation

The N-terminal domain of outer membrane protein OprF of Pseudomonas aeruginosa forms a membrane spanning eight-stranded antiparallel beta-barrel domain that folds into a membrane channel with low conductance. The structure of this protein has been modeled after the crystal structure of the homologous protein OmpA of Escherichia coli. A number of molecular dynamics simulations have been carried out for the homology modeled structure of OprF in an explicit molecular model for the rough lipopolysaccharide (LPS) outer membrane of P. aeruginosa. The structural stability of the outer membrane model as a result of the strong electrostatic interactions compared with simple lipid bilayers is restricting both the conformational flexibility and the lateral diffusion of the porin in the membrane. Constricting side-chain interactions within the pore are similar to those found in reported simulations of the protein in a solvated lipid bilayer membrane. Because of the strong interactions between the loop regions of OprF and functional groups in the saccharide core of the LPS, the entrance to the channel from the extracellular space is widened compared with the lipid bilayer simulations in which the loops are extruding in the solvent. The specific electrostatic signature of the LPS membrane, which results in a net intrinsic dipole across the membrane, is found to be altered by the presence of OprF, resulting in a small electrically positive patch at the position of the channel.

Ultrafast Dynamics of Laser-Excited Solids

AbstractWe discuss recent experimental and theoretical results on ultrafast materials dynamics. Intense, femtosecond lasers can deposit energy in a time that is short compared with relaxation processes and can generate extremely large carrier densities that drive bond softening, nonthermal melting, and ablation. In particular, we present optical experiments on electronic softening of coherent phonons in bismuth and x-ray experiments on ultrafast disordering in indium antimonide that probe the bonding of the lattice under successively higher carrier concentrations. We review a number of molecular dynamics simulations and their assumptions, which address nonthermal melting. Large-scale molecular dynamics simulations elucidate the role of void formation in laser ablation.

Orientation restraints in molecular dynamics simulations using time and ensemble averaging

In this article we present methodology for simulating protein dynamics while imposing restraints derived from NMR measurements on partially ordered molecules. Such measurements may include residual dipolar couplings and chemical-shift anisotropies. We define a restraint potential for use in molecular dynamics and energy minimization. The presented potential is consistent with the simultaneously optimized molecular order tensor. Restraining can be performed with time and ensemble averaging. We performed a large number of molecular dynamics simulations of the histidine containing phosphocarrier protein with restraints on backbone N–H vector orientations derived from residual dipolar couplings. From these simulations it is evident that the use of time- or ensemble-averaged restraints is essential to leave the fluctuations of the restrained vectors unaffected. Without averaging the fluctuations of the restrained vectors are reduced significantly. This also has the effect of decreasing the apparent molecular order-parameter tensor.

Domain synchronization of a cyclic Lotka–Volterra reaction diffusion mechanism

The domain synchronization of a cyclic Lotka–Volterra reaction diffusion mechanism is studied in a dilute solution. This is done by performing a number of molecular dynamics simulations, and by setting up a mathematical model that can account for the dynamics seen in the simulations. The simulations show that below a certain critical length the domain is synchronized, but for larger domains the system quickly becomes unsynchronized. A mathematical model based on the law of mass action and Fick’s law will not reveal this critical length. However, if an ad hoc Gaussian noise term is added to the model, it resemble the synchronization phenomena seen in the molecular dynamics simulations.

Molecular Dynamics Simulation of CaF2Structure in Crystalline, Superionic and Molten Phases

Abstract We describe here a number of molecular dynamics simulations on calcium fluoride over a range of temperatures spanning the transitions to the superionic and molten state. The simulation temperatures are 1400, 1590, 1800, 2000, 2200 K. By using the bond spherical harmonics method with equal neighbor number, we have studied the structure and bond orientation of cation sublattice and anion sublattice in superionic conductor CaF2. The bond order parameters Q1 have been calculated both for the computer generated instataneous configurations from the simulation system and for the standard configurations from the normal distribution model of bond orientation. The comparison of Q1 between the molecular dynamics simulation and the normal distribution model shows that not only the cation sublattice but also the anion sublattice can be described by the normal distribution model. The cations keep their original fcc frame, but in the anion case there is a great deal of random distortions from the original anion...

Conformation and flexibility of oligonucleotides containing O 6-methylguanine: a molecular dynamics study

A number of molecular dynamics simulations on oligonucleotides containing the alkylated base O6-methylguanine have been undertaken in order to determine the conformation and flexibility of such sequences. The conformations of a normal Watson–Crick and a G·T mismatch sequence, generated using a similar simulation procedure, are entirely consistent with the available experimental data. The sequence containing methyl-guanine base-paired with thymine (MeG·T) is iso-structural with the normal sequence irrespective of the conformation of the methyl group but with only two hydrogen bonds between the bases. In contrast, the conformation of the sequence containing methylguanine base-paired with cytosine (MeG·C) depends on the orientation of the methyl group and shows clear differences to the normal Watson–Crick hydrogen bonding. The methylated sequences have different flexibilities, with that containing methylguanine base-paired with cytosine being the more flexible. In summary, we find that the sequence containing MeG·T is closer to the normal sequence than that containing MeG·C and therefore is less likely to be recognised by repair enzymes.

A molecular dynamics study of the hard dumb-bell system

We have carried out a number of molecular dynamics simulations of the diatomic fused hard sphere or hard dumb-bell system. We have studied a selection of state points in the liquid phase, and also examined a plastic crystal and a normal crystal state. Our simulations provide details of time correlation functions for linear and angular velocities and orientational functions, which are compared with standard J-diffusion and truncated cumulant expansion approximations. We also examine the distribution of collision points on the molecular surface. At high density and high bond length, side-on collisions are favoured, while at low density and bond lengths the collisions are distributed more uniformly. Site-superposition approximations are moderately successful at reproducing these results for elongated molecules.