Classical Molecular Dynamics Method Research Articles

Numerical Analysis of the Influence of a Magnetic Field on the Group Dynamics of Iron-Doped Carbon Nanotori

Columnar phases consisting of a group of carbon toroidal molecules (C120, C192, C252, C288) are studied numerically. Each nanotorus was previously doped with an iron atom. This made it possible to use an external magnetic field as a tool for influencing both an individual molecule and a linear fragment of the columnar phase. A high-precision scheme for calculating the dynamics of large molecules with a rigid frame structure is proposed to solve the problem. The group dynamics of nanotori clusters under the influence of an external magnetic field has been studied using classical molecular dynamics methods. The influence of the molecular cluster size, temperature, magnetic moment of the molecule, and magnetic field direction on the collective behavior of iron-doped toroidal molecules with different contents of carbon atoms is analyzed. Molecular dynamics calculations showed that systems of nanotori doped with a single iron atom retain a columnar structure both in the absence and in the presence of an external magnetic field. The columnar fragment behaves as a stable linear association of molecules even at sufficiently high values of magnetic induction, performing a coordinated collective orbital rotation around a common center of mass on a nanosecond time scale.

Threshold and structure of HCP/FCC nucleation in BCC iron under arbitrary triaxial compression: Atomistic simulations

Shear deformation is one of the primary factors determining the threshold of structural transition (ST). It is important to quantitatively express the relationship between shear deformation and the ST threshold for the in-depth development of ST dynamics models. This work used classical molecular dynamics methods to study the effect of shear deformation on the ST in iron by controlling the triaxial ratio of compression. Based on the simulation results, there are significant differences in the microstructure for different loading paths. HCP lamellar twins, HCP-FCC thin twins, and crossed HCP twins embedded with parallel hexahedral FCC grains were all observed, as uniaxial loading transitions to equiaxial loading. The changes in pressure and the softening range of shear stress under different triaxial compressions were revealed. By introducing the strain triaxiality (i.e., the ratio of shear strain to volumetric strain), a general expression of the pressure threshold of ST under high strain rate compression-shear was proposed. In addition, whether the close-packed plane is easily activated under different strain environments was explained based on the results of stacking fault energy.

Molecular Dynamics Study of Friction between Ag Nanoparticle and Two-Dimensional Titanium Carbide Ti2C (MXene)

We report the results of atomistic simulations of friction between two-dimensional titanium carbide Ti2C (MXene) and a silver nanoparticle located on its surface. Numerical experiments were performed within classical molecular dynamics methods using a previously developed scheme for simulations of interactions between MXenes and metal nanoparticles. In the computer experiments performed, both tangential and shear forces were applied to the Ag nanoparticle to initiate its sliding on the surface of the Ti2C MXene. During the simulations, the nanotribological parameters of the studied system, such as the friction force, contact area, friction coefficient, and tangential shear, were computed. It is shown that, for the studied system, the friction coefficient does not depend on the velocity of nanoparticle movement or the contact area. Additionally, the sliding friction of the nanoparticle on the flexible substrate was considered. The latter case is characterized by a larger friction coefficient and contact area due to the formation of wrinkles on the surface of the substrate.

The study of the convergence conditions of the deep-learning method in the Li10GeP2S12 solid state electrolyte system

Abstract The ionic conductivity of solid-state electrolytes at room temperature is crucial for commercializing lithium-ion batteries with solid-state electrolytes. Ab initio methods encounter a challenge due to their substantial computational resource demands. Classical molecular dynamics methods, on the other hand, are suitable for large-scale systems with simulation times reaching the nanosecond scale. However, they rely on empirical parameters in force fields, limiting their use to systems with well-established and extensively validated parameters, which is a constraint in studying new materials. A simulation approach combines ab initio simulations and classical molecular dynamics. Deepmd-kit, a deep learning tool, trains a tailored force field model using ab initio simulation data for the target system. However, as an approximate method, its reliability must be compared with ab initio results. As a data-sensitive method, the amount of data required to achieve the desired accuracy varies for different systems and must be tested accordingly. This paper performs convergence training for system size and simulation time concerning Li10GeP2S12 solid-state electrolytes, establishing the convergence criteria for deep learning data in this system.

A data-guided approach for the evaluation of zeolites for hydrogen storage with the aid of molecular simulations.

This study employs a data-guided approach to evaluate zeolites for hydrogen storage, utilizing molecular simulations. The development of efficient and practical hydrogen storage materials is crucial for advancing clean energy technologies. Zeolites have shown promise as potential candidates due to their unique porous structure and tunable properties. However, the selection and design of suitable zeolites for hydrogen storage remain challenging. Therefore, this work aims to address this materials science question by utilizing molecular simulations and data-guided approaches to evaluate zeolites' performance for hydrogen storage. The results obtained from this study provide valuable insights into the evaluation of zeolites for hydrogen storage. Through molecular simulations, we analyze the adsorption behavior of hydrogen molecules in various zeolite structures. The performance of different zeolite frameworks in terms of hydrogen storage capacity, adsorption energy, and diffusion properties is assessed. Linde type A zeolite (LTA) had the highest capacity with a hydrogen capacity of 4.8wt% out of the 233 investigated zeolites. Furthermore, we investigate the influence of different factors such as mass (M), density (D), helium void fraction (HVF), accessible pore volume (APV), gravimetric surface area (GSA), and largest overall cavity diameter (Di) on the hydrogen storage performance of zeolites. The results show that Di, D, and M have a negative effect on the percentage weight capacity, while GSA and VSA have the highest positive contribution to the percentage weight. This study, therefore, provides new insights into the factors that affect their hydrogen storage capacity by exhibiting the importance of considering multiple factors when evaluating the performance of zeolites and demonstrates the potential of combining different computational methods to provide a more comprehensive understanding of materials. The current study contributes to the understanding of zeolite-based materials for hydrogen storage applications, aiding in the development of more efficient and practical hydrogen storage systems. Computational techniques were employed to investigate the hydrogen storage properties of zeolites. Molecular simulations were performed using classical force fields and molecular dynamics methods. The calculations were carried out at a force field level of theory with the GGA functional. To accurately capture the thermodynamics and kinetics of hydrogen adsorption, enhanced sampling techniques such as Monte Carlo simulations and molecular dynamics with metadynamics were utilized. We employed Grand Canonical Monte Carlo (GCMC) simulations to model hydrogen adsorption in zeolite structures for hydrogen storage. Our approach involved performing a substantial number of Monte Carlo steps (10,000) to ensure system equilibration and precise results. We defined a cutoff distance for particle interactions as 12.5 Ǻ and considered 0.000e framework charge per cell and 0.000e sorbate charge in energy calculations. The choice of an appropriate simulation cell size (50 × 50 × 50) Ǻ was crucial, mirroring real-world conditions. We specified lower and upper fugacity values (1 to 10atm) to capture the range of gas pressures in the simulations. These methodical steps collectively enabled us to accurately model hydrogen adsorption within zeolites, forming the core of our hydrogen storage evaluation. In this research, we utilized DFT calculations to thoroughly investigate the interactions between zeolites and hydrogen. We employed pseudopotentials to describe electron behavior in zeolite systems, choosing them in line with DFT norms and basis set compatibility. Our simulation cell design replicated zeolite periodicity and eliminated boundary effects. Pre-geometry optimization was performed with HyperChem29, ensuring stable conformations with strict convergence criteria. We utilized 6-31 + G(d) and LanL2DZ basis sets for light and heavy atoms, aligning with field standards for computational efficiency and precision. A machine learning algorithm was used to rank the importance of various structural features such as mass (M), density (D), helium void fraction (HVF), accessible pore volume (APV), gravimetric surface area (GSA), and largest overall cavity diameter (Di) and how they affect the capacity of the zeolites. Machine learning analysis was performed with the Scikit-learn library, an open-source Python tool. We employed a range of machine learning models, including SVMs, random forests, and neural networks, primarily for data analysis and feature extraction. Pearson correlation analysis, a classical statistical technique, was used to evaluate linear relationships between variables and assess the strength and direction of these relationships. It served as a complementary tool to understand the interplay of variables in our dataset, distinguishing it from machine learning algorithms. Further quantum chemical calculations were also performed to calculate the adsorption energy, global reactivity electronic descriptors, and natural bond orbital analysis in order to provide insights into the interaction of the zeolites with hydrogen. The simulations and data analysis were performed using BIOVIA material studio software, Gaussian, and Origin Pro software.

An optimization-based discrete element model for dry granular flows: Application to granular collapse on erodible beds

Erosion processes and the associated static/flowing transition in granular flows are still poorly understood despite their crucial role in natural hazards such as landslides and debris flows. Continuum models do not yet adequately reproduce the observed increase of runout distance of granular flows on erodible beds or the development of waves at the bed/flow interface. Discrete Element Methods, which simulate each grain's motion and their complex interactions, provide a unique tool to investigate these processes numerically. Among them, Convex Methods (CM), resulting from the convexification of Contact Dynamics methods, benefit from a robust theoretical framework, ensuring the convergence of the numerical solution at every time iteration. They are also intrinsically more stable than classical Molecular Dynamics methods. However, although already implemented in engineering fields, CMs have not yet been tested in the framework of flows on erodible beds. We present here a Convex Optimization Contact Dynamics (COCD) method and prove that it generates a numerical solution verifying Coulomb's law at each contact and iteration. After its calibration and validation with experiments and another widely used Contact Dynamics method, we show that our simulations accurately reproduce qualitative and even many quantitative characteristics of experimental granular flows on erodible beds, including the increase of runout distance with the thickness of the erodible bed, the change of the static/flowing interface and the presence of erosion waves behind the flow front. Beyond erosion processes, our article endorses CMs as potential accurate tools for exploring complex granular mechanisms.

Molecular Dynamics Modeling of the Grade E Borosilicate Glass Structure Using a Crystal Structural Template

A new method for molecular dynamics (MD) modeling of the glass structure using a crystal structural template is proposed. The template is based on the unit cell of the crystalline phase, whose composition is qualitatively similar to the modeled glass. Using this approach and multistage MD simulation, the model of the spatial structure of grade E borosilicate glass, reproducing its physicochemical characteristics, is obtained. The proposed method enables to model the glass structure using classical MD methods with greater productivity and stability.

Review of deep learning algorithms in molecular simulations and perspective applications on petroleum engineering

In the last few decades, deep learning (DL) has afforded solutions to macroscopic problems in petroleum engineering, but mechanistic problems at the microscale have not benefited from it. Mechanism studies have been the strong demands for the emerging projects, such as the gas storage and hydrate production, and for some problems encountered in the storage process, which are common found as the chemical interaction between injected gas and mineral, and the formation of hydrate. Emerging advances in DL technology enable solving molecular dynamics (MD) with quantum accuracy. The conventional quantum chemical method is computational expensive, whereas the classical MD method cannot guarantee high accuracy because of its empirical force field parameters. With the help of the DL force field, precision at the quantum chemistry level can be achieved in MD. Moreover, the DL force field promotes the computational speed compared with first-principles calculations. In this review, the basic knowledge of the molecular force field and deep neural network (DNN) is first introduced. Then, three representative open-source packages relevant to the DL force field are introduced. As the most common components in the development of oil and gas reservoirs, water and methane are studied from the aspects of computational efficiency and chemical reaction respectively, providing the foundation of oil and gas researches. However, in the oil and gas problems, the complex molecular topo structures and various element types set a high challenge for the DL techniques in MD. Regarding the computational efficiency, it needs improvement via GPU and parallel accelerations to compete with classical MD. Even with such difficulties, the booming of this technique in the area of petroleum engineering can be predictable.

PLAT domain protein 1 (PLAT1/PLAFP) binds to the Arabidopsis thaliana plasma membrane and inserts a lipid

Robust agricultural yields depend on the plant’s ability to fix carbon amid variable environmental conditions. Over seasonal and diurnal cycles, the plant must constantly adjust its metabolism according to available resources or external stressors. The metabolic changes that a plant undergoes in response to stress are well understood, but the long-distance signaling mechanisms that facilitate communication throughout the plant are less studied. The phloem is considered the predominant conduit for the bidirectional transport of these signals in the form of metabolites, nucleic acids, proteins, and lipids. Lipid trafficking through the phloem in particular attracted our attention due to its reliance on soluble lipid-binding proteins (LBP) that generate and solubilize otherwise membrane-associated lipids. The Phloem Lipid-Associated Family Protein (PLAFP) from Arabidopsis thaliana is generated in response to abiotic stress as is its lipid-ligand phosphatidic acid (PA). PLAFP is proposed to transport PA through the phloem in response to drought stress. To understand the interactions between PLAFP and PA, nearly 100 independent systems comprised of the protein and one PA, or a plasma membrane containing varying amounts of PA, were simulated using atomistic classical molecular dynamics methods. In these simulations, PLAFP is found to bind to plant plasma membrane models independent of the PA concentration. When bound to the membrane, PLAFP adopts a binding pose where W41 and R82 penetrate the membrane surface and anchor PLAFP. This triggers a separation of the two loop regions containing W41 and R82. Subsequent simulations indicate that PA insert into the β-sandwich of PLAFP, driven by interactions with multiple amino acids besides the W41 and R82 identified during the insertion process. Fine-tuning the protein-membrane and protein-PA interface by mutating a selection of these amino acids may facilitate engineering plant signaling processes by modulating the binding response.

Biophysical assessment of amantadine and SDS surfactant mixture onto boron nitride nanotube: a molecular dynamics investigation.

The aggregation and adsorption of amantadine and sodium dodecyl sulfate on the boron nitride nanotubes in aqueous system were investigated, employing the classical molecular dynamics method. The aqueous system containing amantadine and sodium dodecyl sulfate was investigated by self-diffusion of the molecules, with particular emphasis on their center of mass mean square displacement. The radial distribution function and dipole moment measurement were used to analyze the water effect on the aggregation and molecular interaction between amantadine and sodium dodecyl sulfate in the complex. In turn, the amantadine molecules are able to develop water monolayers at the aqueous solution but show no noticeable aggregated adsorption in the fluid. In contrast, sodium dodecyl sulfate self-aggregation on the boron nitride nanotube efficiently occurs, and so the effect of sodium dodecyl sulfate on aggregated adsorption of amantadine was studied. Our results show that in the presence of sodium dodecyl sulfate at critical micelle concentration, its effect on the aggregation of amantadine was enhanced onto boron nitride nanotube compared to just pure sodium dodecyl sulfate or amantadine on boron nitride nanotube. Our simulations show a remarkable restructuring and enhanced orientation of the water molecules around sodium dodecyl sulfate and amantadine adsorbed on boron nitride nanotube. All molecular dynamics simulations were done using NAMD-2.9 package with CHARMM-36 force field. The nanotube PDB file was made by VMD with (12, 12) indexes. The nanotube atoms were considered to be fixed and periodic during the simulation process, and the ends did not cap with hydrogens. The initial PDB file of components was saved from ChemOffice software, and the webservers to obtain the charge and topology files were Reddb and Swiss Param webservers. The VMD software was used for structural visualizations and graphical analysis.

Molecular dynamics simulation of the transmembrane transport process of reactive species under the synergistic effect of plasma oxidation and an electric field

Cold atmospheric pressure plasma (CAP)-assisted cancer therapy has become a popular topic in plasma biomedical research. Membrane lipid oxidation and local electric fields are two important factors in plasma–cell interactions, and the study of their synergistic effect is highly significant for optimizing the regulatory mechanism of the plasma-induced apoptosis of cancer cells. In this paper, a model of oxidized phospholipids was established, and the transmembrane process of reactive species was simulated by the classical molecular dynamics (MD) method under the conditions of oxidation and an electric field. The results showed that hydrophilic reactive oxygen species could not penetrate the membrane lipids through oxidation. The formation of electroporation provided a new channel for reactive species to penetrate the membrane, and the oxidation effect reduced the electric field threshold of membrane electroporation. Our simulation could provide theoretical support for the plasma-induced apoptosis of cancer cells at the microscopic level, provide mechanistic guidance for the practical application of plasma-induced cancer therapy, and promote the development of CAP in the field of cancer therapy.

Machine learning‐assisted study of correlation between post‐transition‐state bifurcation and initial phase information at the ambimodal transition state

AbstractThe Diels–Alder cycloaddition of cyclopentadiene and nitroethene, the intramolecular cycloaddition between a diene and triene, and the Diels–Alder cycloaddition of 2‐hydroxyacrolein with 1,3‐butadiene involving post‐transition‐state bifurcation (PTSB) were studied. These cycloaddition reactions were investigated using quasi‐classical trajectory (QCT), classical molecular dynamics (MD), ring‐polymer molecular dynamics (RPMD) simulations, and supervised machine‐learning binary classification techniques. Room‐temperature dynamics simulations started from the ambimodal transition state (TS) using the QCT, classical MD, and RPMD methods presented similar dynamics. Binary classification revealed that the initial geometry displacement from the ambimodal TS for the Diels–Alder cycloaddition of cyclopentadiene and nitroethene contributed to the branching dynamics and that the initial momenta for the intramolecular cycloaddition between a diene and triene and the Diels–Alder cycloaddition of 2‐hydroxyacrolein with 1,3‐butadiene played a significant role in the bifurcation dynamics.

Effect of Hydration on the Intermolecular Interaction of Various Quaternary Ammonium Based Head Groups with Hydroxide Ion of Anion Exchange Membrane Studied at the Molecular Level

Currently, the main limitation of Anion Exchange Membrane Fuel Cells is related to their low chemical stability under alkaline conditions due to the degradation of quaternary ammonium-based head groups, which lowers the transportation of hydroxide ions as well. The knowledge of the intermolecular interaction of various quaternary ammonium head groups with hydroxide ions is the key to improving hydroxide ion’s diffusivity and chemical stability of various quaternary ammonium-based head groups. Consequently, the intermolecular interaction of hydroxide ions with different quaternary ammonium head groups of anion exchange membranes is investigated at the different hydration levels via classical all-atom Molecular Dynamics and molecular well-tempered MetaDynamics simulation methods in this work. Several quaternary ammonium head groups (a) pyridinium, (b) 1,4-diazabicyclo [2.2.2] octane, (c) benzyltrimethylammonium, (d) n-methyl piperidinium, (e) guanidium, and (f) trimethylhexylammonium were investigated in detail. Classical all-atom molecular dynamic simulations illustrate that the results of radial distribution function between the nitrogen atoms of six different quaternary ammonium head groups and hydroxide ion are as follows: (a) > (c) ≥ (f) > (d) > (e) > (b). In addition, from the diffusion coefficient values it was found that the mobility of hydroxide ion by quaternary ammonium head group (f) was lower than (c) at the different hydration levels.

The study of ionic conductivity of the Li10GeP2S12 type Solid State Electrolyte by an extrapolation method and a deep-learning method

An extrapolation method is usually applied when Ab initio molecular dynamics (AIMD) is applied to studying ionic conductivity in solid-state electrolytes in lithium-ion batteries. As the ions move slowly in solid-state electrolytes, the first-principles method typically involves computationally intensive calculations, and it can take significant time to obtain accurate results. First-principles method is too expensive for the time scale required at room temperature. The classical molecular dynamics method is typically applied to systems containing thousands of atoms and can simulate the system’s behavior over nanoseconds. During the simulation, the positions and velocities of the atoms are updated at discrete time intervals, allowing the system’s behavior to be studied over time. However, its accuracy depends on the empirical force-field libraries. Limited by the computational resource, the previous studies applied the extrapolation method to obtain the room temperature ionic conductivity, which was not accurate because the linear relationship in the Arrhenius equation was not valid in a wide range of temperatures. Deepmd-Kit is a tool that integrates these two different computational approaches. The extrapolation and Deepmd methods were applied to the materials Li10GeP2S12, Li10SiP2S12, Li10GePS12Cl, and Li10SiPS12Cl, respectively. Both methods showed that the lithium ions favor the c direction when diffusing in the LGPS-type solid-state electrolytes. The ionic conductivity is more accurate with the dependent method compared with experiments.

Molecular Dynamics Simulation of Diisopropyl Ether Using Various Interatomic Potentials

A comparative assessment of the accuracy of determining the density and viscosity has been carried out for diisopropyl ether using the method of classical molecular dynamics using three potentials. The accuracy of determining the viscosity coefficients when using equilibrium and nonequilibrium calculation methods was also investigated.

Comparative analysis of molecular interactions in quaternary fluid system performed by classical and ab initio molecular dynamics

Molecular interactions in the quaternary fluid system acetic acid–n-propanol–n-propyl acetate–water were analyzed by classical and ab initio molecular dynamics methods. It was shown that ab initio molecular dynamics simulation can reproduce the molecular mobility tendency and structural features of a multicomponent system without empirical parameters.

Transportation and deformation of high-speed aluminum nanoparticles in inert gas with molecular dynamics study

In addition to complex deformation, high-speed nanoparticles in gas are also accompanied by significant size and interfacial effects. In this work, we simulate the transportation behavior of high-speed aluminum nanoparticles in helium gas with the classical molecular dynamics method. The evolution of aerothermodynamic quantities of solid particles and liquid particles is revealed, and different temperature rise effects are found. Furthermore, the melting of aluminum particles induced by high aerodynamic drag force is discovered, and the melting threshold conditions are proposed. In low-density (0.002 g/cm3) and high-density (0.02 g/cm3) gas, the initial velocity at which particles start to melt is 6 and 4 km/s, respectively. During the deformation of solid particles, the evolution of dislocation motion is discussed, and the evolution of the development characteristics of the molten layer is given. During the deformation of the liquid particles, vibration deformation and bag deformation modes are observed. The threshold conditions for deformation mode transitions are also given. Only in high-density gas, bag deformation occurs when the initial velocity of particles (D > 5 nm) exceeds 6 km/s. The local mechanical quantity of gas is used to explain the variation of the drag force of the particles. Moreover, the drag force model is corrected according to temperature and deformation effects. Within a certain period, the model results overestimate the drag force, and the error with the simulation results is about 25%. This provides a model reference for high-speed nanoparticle dynamics and two-phase flow problems.

The physicochemical properties of the CeF3 – FLiNaK molten mixture: an in silico study

ABSTRACT This study is devoted to the physicochemical properties of CeF3 – FLiNaK molten mixture with various CeF3 concentrations. Density, local structure, mean ion pair lifetimes, heat capacity, self-diffusion coefficients and viscosity were calculated using the classical molecular dynamics method. Most of the properties were studied in the temperature range of 950–1200 K. Taking into consideration data obtained both in this study and in the previous ab initio simulations we quantitatively describe the local structure dynamics. Specifically, the stability of [CeFn] groupings is discussed in detail. To ensure the reliability of the results obtained, the pair potentials for the Ce3+-Ce3+ and Ce3+-F− pairs were carefully fitted through the two-stage algorithm consisting of ab initio energy calculation and fine stochastic optimization.

Investigation of Solid Formation Enthalpy and Molecular Mechanics Energies of Amino Acids via Force Field Approach

Accurate determination of the thermodynamic and molecular mechanical properties of amino acids will contribute to a better understanding of their folding mechanisms. In this study, the enthalpy values and molecular mechanics parameters of 17 amino acids were investigated by the classical molecular dynamics method. All calculations were performed using the force-field potential approach. As a result, the calculated solid formation enthalpy for ALA, ASN, ASP, CYS, LYS, and PHE are in good agreement with the experimental data. In addition, molecular mechanics parameters such as Coulomb, bond, angle, dihedral, and Van der Waals were calculated for all amino acids. It is seen that the Coulomb energy is quite low compared to the rest of the molecular mechanical energies. The molecular mechanical energies obtained from the study will contribute to protein-lipid modification studies for electronic interaction, ligand binding to the cell surface, and correct protein localization.

Nanostructure, Plastic Deformation, and Influence of Strain Rate Concerning Ni/Al2O3 Interface System Using a Molecular Dynamic Study (LAMMPS)

The plastic deformation mechanisms of Ni/Al2O3 interface systems under tensile loading at high strain rates were investigated by the classical molecular dynamics (MD) method. A Rahman-Stillinger-Lemberg potential was used for modeling the interaction between Ni and Al atoms and between Ni and O atoms at the interface. To explore the dislocation nucleation and propagation mechanisms during interface tensile failure, two kinds of interface structures corresponding to the terminating Ni layer as buckling layer (Type I) and transition layer (Type II) were established. The fracture behaviors show a strong dependence on interface structure. For Type I interface samples, the formation of Lomer-Cottrell locks in metal causes strain hardening; for Type II interface samples, the yield strength is 40% higher than that of Type I due to more stable Ni-O bonds at the interface. At strain rates higher than 1×109 s-1, the formation of L-C locks in metal is suppressed (Type I), and the formation of Shockley dislocations at the interface is delayed (Type II). The present work provides the direct observation of nucleation, motion, and reaction of dislocations associated with the complex interface dislocation structures of Ni/Al2O3 interfaces and can help researchers better understand the deformation mechanisms of this interface at extreme conditions.