Framework Of Molecular Dynamics Research Articles

Effects of graphene reinforcement on morphology, thermophysical, and mechanical properties of polyvinyl alcohol and polyacrylamide in condensed phases

The subtle interplay of non-covalent interaction in enhancing the compatibility between the graphene-based material and the oligomers of polyvinyl alcohol (PVA) and polyacrylamide (PAM) in the solvent phase is unraveled within the framework of all-atom classical force field-based molecular dynamics (MD) simulations. The decomposition of binding free energy analysis demonstrates that the interaction between polymer segments and graphene (G)-surface crucially emanates from the van der Waals (vdW) interactions, while the adsorption of polymer chains on the graphene oxide (GO)-surface is influenced by vdW and electrostatic interactions. The influence of diverse factors including the strain rate, the size of the nanofiller, the number of oligomers, and the thermal quenching rate on controlling the morphology, mechanical, and thermophysical properties of the graphene-reinforced polymer nanocomposites are further explored. The uniaxial deformation simulations of the graphene-reinforced PVA and PAM matrices show that the interfacial mechanical strength is escalated for the G-PVA nanocomposite. The inclusion of graphene nanofiller reduces the polymer chain mobility and increases the intermolecular interaction, which in turn enhances the toughness of the graphene-polymer composites. Increasing the strain rate raises the yield strength and modulus, making the composites appear stronger and stiffer. As evidenced by the predicted glass transition temperature, the thermal stability of the PVA and PAM matrices is significantly improved by the graphene reinforcement.

A lamellar-morphology-based computational modeling for predicting the thermal conductivity of semicrystalline polymers

Molecular-scale design of crystal structures is an emerging approach for significantly improving the intrinsic properties of polymers. In this study, we performed computational modeling to quantitatively predict the thermal conductivity of polymers based on their crystal morphology. Polyethylene lamellae with alternating crystalline and amorphous phases were prepared using isothermal crystallization within a coarse-grained molecular dynamics framework. The crystalline bulk, transient, and amorphous regions were clearly distinguished based on the distribution of the Steinhardt-bond order parameter. The thermal conductivity of the local regions was estimated discretely by applying a steady heat flow, facilitating a bottom-up prediction of the thermal response depending on the morphology of the lamellae. In particular, the integration with homogenization theory can evaluate the effective thermal conductivity of the system in terms of crystallinity, temperature, lamella thickness and orientation. The modeling successfully provided quantitative predictions for two representative hierarchical structures: spherulites and oriented crystals. Therefore, this study serves as a theoretical guide for molecular-level rational design of semicrystalline materials with high thermal conductivity.

Process dependent properties of glassy polymer films revealed by molecular dynamics simulations

A molecular dynamics (MD) framework has been proposed to systematically simulate process (but not cooling rate) dependent properties of glassy polymer films. With the coarse-grained (CG) polylactide (PLA) of low molecular weight (MW), the simulated thermal properties of glassy films are confirmed to be surely decided by the routes to prepare them. While the glassy films initially prepared from the melt bulk are stable, the glassy film initially constructed from the glassy bulk appears to be unstable, featuring highest potential energy among those films and different averaged heat capacity from the bulk counterparts. Inspired by physical vapour deposition (PVD), a surface formation process is further applied to the unstable film to generate stable glassy polymer films. Similar to the PVD, an optimal temperature at which the film is most stable can be identified with lower potential energy than the conventional films, for which free surface and enhanced order of the middle layer are responsible. This work paves a way for obtaining simulated models of stable glassy films and experimentally preparing advanced glassy films for high MW polymers, which otherwise are hard to be realized by the PVD process.

Quasi-Classical Trajectory Calculation of Rate Constants Using an Ab Initio Trained Machine Learning Model (aML-MD) with Multifidelity Data.

Machine learning (ML) provides a great opportunity for the construction of models with improved accuracy in classical molecular dynamics (MD). However, the accuracy of a ML trained model is limited by the quality and quantity of the training data. Generating large sets of accurate ab initio training data can require significant computational resources. Furthermore, inconsistent or incompatible data with different accuracies obtained using different methods may lead to biased or unreliable ML models that do not accurately represent the underlying physics. Recently, transfer learning showed its potential for avoiding these problems as well as for improving the accuracy, efficiency, and generalization of ML models using multifidelity data. In this work, ab initio trained ML-based MD (aML-MD) models are developed through transfer learning using DFT and multireference data from multiple sources with varying accuracy within the Deep Potential MD framework. The accuracy of the force field is demonstrated by calculating rate constants for the H + HO2 → H2 + 3O2 reaction using quasi-classical trajectories. We show that the aML-MD model with transfer learning can accurately predict the rate constants while reducing the computational cost by more than five times compared to the use of more expensive quantum chemistry training data sets. Hence, the aML-MD model with transfer learning shows great potential in using multifidelity data to reduce the computational cost involved in generating the training set for these potentials.

Elucidating the role of diverse mineralisation paradigms on bone biomechanics - a coarse-grained molecular dynamics investigation.

Bone as a hierarchical composite structure plays a myriad of roles in vertebrate skeletons including providing the structural stability of the body. Despite this critical role, the mechanical behaviour at the sub-micron levels of bone's hierarchy remains poorly understood. At this scale, bone is composed of Mineralised Collagen Fibrils (MCF) embedded within an extra-fibrillar matrix that consists of hydroxyapatite minerals and non-collagenous proteins. Recent experimental studies hint at the significance of the extra-fibrillar matrix in providing the bone with the stiffness and ductility needed to serve its structural roles. However, due to limited resolution of experimental tools, it is not clear how the arrangement of minerals, and in particular their relative distribution between the intra- and extra-fibrillar space contribute to bone's remarkable mechanical properties. In this study, a Coarse Grained Molecular Dynamics (CGMD) framework was developed to study the mechanical properties of MCFs embedded within an extra-fibrillar mineral matrix and the precise roles extra- and intra-fibrillar mineralisation on the load-deformation response was investigated. It was found that the presence of extra-fibrillar mineral resulted in the development of substantial residual stress in the system, by limiting MCF shortening that took place during intra-fibrillar mineralisation, resulting in substantial compressive residual stresses in the extra-fibrillar mineral phase. The simulation results also revealed the crucial role of extra-fibrillar mineralisation in determining the elastic response of the Extrafibrillar mineralised MCF (EFM-MCF) system up to the yield point, while the fibrillar collagen affected the post-yield behaviour. When physiological levels of mineralisation were considered, the mechanical response of the EFM-MCF systems was characterised by high ductility and toughness, with micro-cracks being distributed across the extra-fibrillar matrix, and MCFs effectively bridging these cracks leading to an excellent combination of strength and toughness. Together, these results provide novel insight into the deformation mechanisms of an EFM-MCF system and highlight that this universal building block, which forms the basis for lamellar bone, can provide an excellent balance of stiffness, strength and toughness, achieving mechanical properties that are far beyond the capabilities of the individual constituents acting alone.

Water and ions in electrified silica nano-pores: a molecular dynamics study.

Solid-liquid interfaces (SLIs) are ubiquitous in science and technology from the development of energy storage devices to the chemical reactions occurring in the biological milieu. In systems involving aqueous saline solutions as the liquid, both the water and the ions are routinely exposed to an electric field, whether the field is externally applied, or originating from the natural surface charges of the solid. In the current study a molecular dynamics (MD) framework is developed to study the effect of an applied voltage on the behaviour of ionic solutions located in a ∼7 nm pore between two uncharged hydrophilic silica slabs. We systematically investigate the dielectric properties of the solution and the organisation of the water and ions as a function of salt concentration. In pure water, the interplay between interfacial hydrogen bonds and the applied field can induce a significant reorganisation of the water orientation and densification at the interface. In saline solutions, at low concentrations and voltages the interface dominates the whole system due to the extended Debye length resulting in a dielectric constant lower than that for the bulk solution. An increase in salt concentration or voltage brings about more localized interfacial effects resulting in dielectric properties closer to that of the bulk solution. This suggests the possibility of tailoring the system to achieve the desired dielectric properties. For example, at a specific salt concentration, interfacial effects can locally increase the dielectric constant, something that could be exploited for energy storage.

Unravelling Mn4Ca cluster vibrations in the S1, S2 and S3 states of the Kok-Joliot cycle of photosystem II.

Vibrational spectroscopy serves as a powerful tool for characterizing intermediate states within the Kok-Joliot cycle. In this study, we employ a QM/MM molecular dynamics framework to calculate the room temperature infrared absorption spectra of the S1, S2, and S3 states via the Fourier transform of the dipole time auto-correlation function. To better analyze the computational data and assign spectral peaks, we introduce an approach based on dipole-dipole correlation function of cluster moieties of the reaction center. Our analysis reveals variation in the infrared signature of the Mn4Ca cluster along the Kok-Joliot cycle, attributed to its increasing symmetry and rigidity resulting from the rising oxidation state of the Mn ions. Furthermore, we successfully assign the debated contributions in the frequency range around 600 cm-1. This computational methodology provides valuable insights for deciphering experimental infrared spectra and understanding the water oxidation process in both biological and artificial systems.

A simulation study of magnetic nanoparticle clustering in a fluid flow

The assembly of magnetic nanoparticles has been promisingly used in biomedical applications. Several computational studies have addressed the study of cluster formation in the colloidal regime. However, these studies did not mention the formation of clusters in nonequilibrium conditions, such as fluid flow. To fill this gap, we develop computational models of magnetohydrodynamic systems to study their behavior within a flow in terms of cluster shape formation. All simulations were performed by Verlet integration through LAMMPS, a molecular dynamics framework. Through simulations, we discover that the fluid flow promotes formation of non-linear clusters, and linear clusters tend to orient toward the direction of the flow. These phenomena are investigated by using the equilibrium of magnetic torque and fluid torque. The analysis shows that the formation of non-linear clusters are mainly assisted by a bias-orientation of the unstable linear clusters, while the bias-orientation is originated from the torque cancellation in the direction of the flow.

Quantifying Stochastic Processes in Shaping Dissolved Organic Matter Pool with High-Resolution Mass Spectrometry.

Natural dissolved organic matter (DOM) represents a ubiquitous molecular mixture, progressively characterized by spatiotemporal resolution. However, an inadequate comprehension of DOM molecular dynamics, especially the stochastic processes involved, hinders carbon cycling predictions. This study employs ecological principles to introduce a neutral theory to elucidate the fundamental processes involving molecular generation, degradation, and migration. A neutral model is thus formulated to assess the probability distribution of DOM molecules, whose frequencies and abundances follow a β-distribution relationship. The neutral model is subsequently validated with high-resolution mass spectrometry (HRMS) data from various waterbodies, including lakes, rivers, and seas. The model fitting highlights the prevalence of molecular neutral distribution and quantifies the stochasticity within DOM molecular dynamics. Furthermore, the model identifies deviations of HRMS observations from neutral expectations in photochemical and microbial experiments, revealing nonrandom molecular transformations. The ecological null model further validates the neutral modeling results, demonstrating that photodegradation reduces molecular stochastic dynamics at the surface of an acidic pit lake, while random distribution intensifies at the river surface compared with the porewater. Taken together, the DOM molecular neutral model emphasizes the significance of stochastic processes in shaping a natural DOM pool, offering a potential theoretical framework for DOM molecular dynamics in aquatic and other ecosystems.

On the missing single collision peak in low energy heavy ion scattering

We present experimental and simulation data on the oblique angle scattering of heavy Sn ions at 14keV energy from a Mo surface. The simulations are performed with the binary collision approximation codes TRIM, TRIDYN, TRI3DYN, SDTrimSP, and IMSIL. Additional simulations were performed in the molecular dynamics framework with LAMMPS. Our key finding is the absence of an expected peak in the experimental energy spectrum of backscattered Sn ions associated with the pure single collision regime. In sharp contrast to this, however, all simulation codes we applied do show a prominent single collision signature both in the energy spectrum and in the angular scatter pattern. We discuss the possible origin of this important discrepancy and show in the process, that widely used binary collision approximation codes may contain hidden parameters important to know and to understand.

Mechanics of Tunable Adhesion With Surface Wrinkles

Abstract Surface wrinkles have emerged as a promising avenue for the development of smart adhesives with dynamically tunable adhesion, finding applications in diverse fields, such as soft robots and medical devices. Despite intensive studies and great achievements, it is still challenging to model and simulate the tunable adhesion with surface wrinkles due to roughened surface topologies and pre-stress inside the materials. The lack of a mechanistic understanding hinders the rational design of these smart adhesives. Here, we integrate a lattice model for nonlinear deformations of solids and nonlocal interaction potentials for adhesion in the framework of molecular dynamics to explore the roles of surface wrinkles on adhesion behaviors. We validate the proposed model by comparing wrinkles in a neo-Hookean bilayer with benchmarked results and reproducing the analytical solution for cylindrical adhesion. We then systematically study the pull-off force of the wrinkled surface with varied compressive strains and adhesion energies. Our results reveal the competing effect between the adhesion-induced contact and the roughness due to wrinkles on enhancing or weakening the adhesion. Such understanding provides guidance for tailoring material and geometry as well as loading wrinkled surfaces for different applications.

Molecular Dynamics Study of Gas–Surface Interactions on -Cristobalite Surface

In the present work, nonreactive gas–surface interactions between nitrogen molecules and a -cristobalite surface are analyzed using the molecular dynamics framework. A sampling method is employed to perform trajectory calculations, and the tangential momentum accommodation coefficient is computed. The credibility of the reactive force field potential to model cristobalite is investigated, and the effect of the surface and gas temperatures on the tangential momentum accommodation coefficient is studied in detail. The obtained value of the tangential momentum accommodation coefficient (from molecular dynamics analysis) is used as an input parameter in the Maxwell gas–surface interaction model using the direct simulation Monte Carlo method to investigate the surface heat flux on the nose region of a model reentry vehicle. The computed heat-flux results obtained using a molecular-dynamics-derived accommodation coefficient are found to be in excellent agreement with the experimental data.

MULTICOMPONENT ALLOYS AND LAYERED COMPOSITE NANOMATERIALS FOR HYDROGEN TECHNOLOGIES

The stability of high entropy alloys (HEA) is of great importance for various applications in many areas. This review covers one of the most topical areas in this area – the creation of stable multicomponent membrane alloys with improved performance. The review presents an analysis of the results of studies of equiatomic and non-equiatomic four- and five-component alloys, which are successfully used as membrane alloys for hydrogen technologies. An effective method for increasing the strength of membrane alloys is a special heat treatment, as a result of which secondary strengthening phases are precipitated and superlattices are formed. In addition, an unusual morphology of micrograins is formed in the form of cuboid blocks with rounded tops, spheroidal and ellipsoidal grains, consisting of hardening thermodynamically stable γ' and γ-phases isolated during heat treatment. Alloying is an important factor in strengthening HEAs. The influence of alloying with Ni or Cr on the mechanical properties of a number of multicomponent compositions has been analyzed. It is shown that Al + Ti or Al + Nb alloying pairs, structured into matrices of solid solutions of membrane alloys, increase their strength, thermal stability, hydrogen kinetics, and resistance to hydrogen embrittlement. Within the framework of molecular dynamics, the effect of strain hardening of membrane HEAs by multiple deformation has been studied and the mechanism for creating a synergistic effect has been established. The review also presents relatively recently obtained hexa- and pentagonal two-dimensional structures with ultrahigh strength and increased thermal stability and excellent photocatalytic properties, such as MX2 dichalcogenides and their pentagonal configurations, as well as two-dimensional alloys Cu1 – xNix, Ti1 – xNix and compounds Bi1 – xSbx. All these materials are effective catalysts for water dissociation and hydrogen concentration. Particular attention is paid to neural network prediction of interatomic potentials as an effective method of theoretical research for the search for new membrane HEAs.

Nanoscale liquid-vapor phase change characteristics of binary mixtures from molecular dynamics perspective

Nanoscale thin liquid film boiling of a binary liquid mixture subjected to extremely rapid heating within molecular dynamics framework is the focus of our study. A three-phase MD simulation domain is considered where liquid argon-krypton (Ar-Kr) binary mixture rests over a solid Platinum (Pt) like substrate. The molecular system starting from freezing state is subjected to non-equilibrium boundary heating following a sufficient equilibration period to induce liquid–vapor phase change process. Depending on the mixture composition and boundary heating rate, four distinct phase change scenarios have been observed. Characteristics of different phase change scenarios have been studied in terms of important system parameters such as transient atomistic distribution, temperature, pressure, evaporation history, onset of liquid cluster splashing from the wall along with atomic kinetics parameters like COM (Center of Mass), and MSD (Mean Square Displacement). Addition of Kr with Ar that is less volatile compared to Ar, has been found to result in a delay in the evaporation onset and decrease in interaction energy. As a consequence, with the increase of Kr fraction in the binary mixture, the mobility of the evaporated atoms is hindered and the onset temperature of liquid splashing from the wall that resembles generation of “Leidenfrost Film” changes. Also, the near wall atomistic energetics is significantly influenced by mixture composition. Finally, a contour of various phase change modes for Ar-Kr binary mixtures has been presented as a function of mixture composition and boundary heating rate. Present work has been found to be aligned with contemporary works in the context of the derived heat transfer properties.

Accurate prediction of heat conductivity of water by a neuroevolution potential.

We propose an approach that can accurately predict the heat conductivity of liquid water. On the one hand, we develop an accurate machine-learned potential based on the neuroevolution-potential approach that can achieve quantum-mechanical accuracy at the cost of empirical force fields. On the other hand, we combine the Green-Kubo method and the spectral decomposition method within the homogeneous nonequilibrium molecular dynamics framework to account for the quantum-statistical effects of high-frequency vibrations. Excellent agreement with experiments under both isobaric and isochoric conditions within a wide range of temperatures is achieved using our approach.

Coarse-grain molecular dynamics simulation framework to unravel the interactions of surfactants on silica surfaces for oil recovery

A coarse-grained molecular dynamics (CG-MD) framework, based on the MARTINI 3.0 model, was developed to characterise the interactions between surfactants and oil-silica substrates to resemble chemical enhanced oil recovery (EOR) processes. Previous computational studies, at the atomistic scale, addressed interactions between surfactants and oil over diverse surfaces. Even though simulations provided significant information involved throughout different stages of oil extraction from surfaces, atomistic scale simulations fail when approaching the time and size scale required to address the surfactant phase behaviour that can also impact the oil detachment. Our coarse-grained model aims to overcome the lack of computer approaches that can tackle the surfactant self-assembly and the formation of ordered structures in the removal of oil from silica substrates. A new MARTINI 3.0 coarse-grain framework to model silica surfaces and aqueous solutions of CiEj and C16TAB surfactants is presented. Coarse-grained simulations entailing a silica surface, covered by dodecane or eicosane were brought in contact with aqueous solutions of C16TAB and four nonionic CiEj (C8E6, C8E12, C12E6, C16E12) surfactants to resemble EOR processes with a size/time scale several orders of magnitude larger than previous simulations. The impact of concentration and hydrophilic-lipophilic balance (HLB) of surfactants on the detachment of dodecane and eicosane from the silica surface was evaluated by visual inspection of the simulation snapshots and the evolution of the solvent accessible surface areas (SASA). In contrast with previous atomistic simulations, nonionic surfactants seem the best candidates for an optimal oil removal from silica-based surfaces whereas the presence of charged moieties hinders the process. Diluted nonionic CE aqueous solutions were shown to be the most effective solutions, unlike more concentrated ones. When compared with dodecane, eicosane was less prone to be removed from the silica surface due to the increased alkyl chain length. Our study demonstrates that not only the surfactant nature but also the phase behaviour, clearly impact the detachment of oil from silica surfaces. This is an important aspect to consider for a proper choice of surfactants in EOR processes, that is only attainable through a coarse-grained framework.

The radiative surface hopping (RSH) algorithm: Capturing fluorescence events in molecular systems within a semi-classical non-adiabatic molecular dynamics framework.

In this article, we present the radiative surface hopping algorithm, which enables modeling fluorescence within a semi-classical non-adiabatic molecular dynamics framework. The algorithm has been tested for the photodeactivation dynamics of trans-4-dimethylamino-4'-cyanostilbene (DCS). By treating on equal footing the radiative and non-radiative processes, our method allows us to attain a complete molecular movie of the excited-state deactivation. Our dynamics rely on a semi-empirical quantum mechanical/molecular mechanical Hamiltonian and have been run for hundreds of picoseconds, both in the gas phase and in isopropyl ether. The proposed approach successfully captures the first fluorescence processes occurring in DCS, and it succeeds in reproducing the experimental fluorescence lifetime and quantum yield, especially in the polar solvent. The analysis of the geometrical features of the emissive species during the dynamics discards the hypothesis of a twisted intramolecular charge transfer state to be responsible for the dual emission observed experimentally in some polar solvents. In a nutshell, our method opens the way for theoretical studies on early fluorescence events occurring up to hundreds of picoseconds in molecular systems.

Molecular dynamics of He encapsulation in the C20H20 cage at the threshold energy

The encapsulation of He inside C20H20 cage has been investigated in the framework of molecular dynamics for incident atom energy in a range close to the threshold value, corresponding to the projectile passing through the pentagonal carbon ring. The entrance of He atom into dodecahedrane is analyzed in terms of static and dynamic potential barriers. The details of helium capture inside the molecular box is revealed by using geometric and energetic parameters, being associated with the response of the atomic configuration near the collision site on the C20H20 cage. The energy transferred from the incident atom to deformation, vibrations, and recoil of the molecule target, was calculated as a function of the distance between the projectile and the collision site on C20H20 cage. The calculations show that the noble gas atom, encapsulated at the threshold of 315.62 Å/ps, oscillates across the void of the dodecahedrane with frequencies of 2.5, 22.5, and 25.0 THz, monitored for a time interval of 10 ps starting at helium penetration into C20H20. Molecular dynamics calculations are based on ab-initio Hartree-Fock method, considering an adiabatic process. This study reveals advantageous features of helium encapsulation in dodecahedrane molecules in designing super-molecular structures for advanced nano sensors in the field of THz detectors.

Driving and characterizing nucleation of urea and glycine polymorphs in water

Crystal nucleation is relevant across the domains of fundamental and applied sciences. However, in many cases, its mechanism remains unclear due to a lack of temporal or spatial resolution. To gain insights into the molecular details of nucleation, some form of molecular dynamics simulations is typically performed; these simulations, in turn, are limited by their ability to run long enough to sample the nucleation event thoroughly. To overcome the timescale limits in typical molecular dynamics simulations in a manner free of prior human bias, here, we employ the machine learning-augmented molecular dynamics framework "reweighted autoencoded variational Bayes for enhanced sampling (RAVE)." We study two molecular systems-urea and glycine-in explicit all-atom water, due to their enrichment in polymorphic structures and common utility in commercial applications. From our simulations, we observe multiple back-and-forth nucleation events of different polymorphs from homogeneous solution; from these trajectories, we calculate the relative ranking of finite-sized polymorph crystals embedded in solution, in terms of the free-energy difference between the finite-sized crystal polymorph and the original solution state. We further observe that the obtained reaction coordinates and transitions are highly nonclassical.

A Dynamic Proton Bond: MH+·H2O ⇌ M·H3O+ Interconversion in Loosely Coordinated Environments.

The interaction of organic molecules with oxonium cations within their solvation shell may lead to the emergence of dynamic supramolecular structures with recurrently changing host-guest chemical identity. We illustrate this phenomenon in benchmark proton-bonded complexes of water with polyether macrocyles. Despite the smaller proton affinity of water versus the ether group, water in fact retains the proton in the form of H3O+, with increasing stability as the coordination number increases. Hindrance in many-fold coordination induces dynamic reversible (ether)·H3O+ ⇌ (etherH+)·H2O interconversion. We perform infrared action ion spectroscopy over a broad spectral range to expose the vibrational signatures of the loose proton bonding in these systems. Remarkably, characteristic bands for the two limiting proton bonding configurations are observed in the experimental vibrational spectra, superimposed onto diffuse bands associated with proton delocalization. These features cannot be described by static equilibrium structures but are accurately modeled within the framework of ab initio molecular dynamics.