Large-scale Molecular Dynamics Simulations Research Articles

Molecular Dynamics Simulation of the Interaction within the Carboxymethyl Cellulose-Modified Montmorillonite Lamellae at Aggressive CuCl2 Concentrations.

To elucidate the degradation mechanism of the CMC-modified MMT composite at aggressive Cu2+ concentrations, large scale molecular dynamics simulation was conducted for CuCl2 concentrations ranging from 0 to 800 mM. Both CMC and MMT followed the Langmuir isotherm for Cu2+ adsorption, and the adsorption capacity of CMC (8.75 mmol/g) was much higher than that of MMT (0.83 mmol/g). Despite the CMC mass ratio being only 4.1%, it adsorbed up to 34.3% of the total adsorbed Cu2+. The Cu2+ attraction ability hierarchy of oxygen-containing functional groups in the CMC is as follows: carboxylic oxygens > alcoholic oxygens > carbinolic oxygens > bridging oxygens > glucose oxygens. Carboxyls were the most effective in chelating and complexing with Cu2+, and they could be intentionally added in artificially synthesized polymer-MMT composites for Cu2+ containment. Formation of the Cu2+ cation bridge between CMC and MMT at aggressive CuCl2 concentrations contributed to the transition of CMC density distribution from unimodality to bimodality and enhanced resistance of polymer elution. As the CuCl2 concentration increased, the stoichiometric ratio between the chelated Cu2+ and carboxylic oxygens increased from 1:2 to 1:1, suggesting the evolution of the Cu2+ chelation mechanism.

Unraveling the Hall-Petch to inverse Hall-Petch transition in nanocrystalline high entropy alloys under shock loading

The transition from Hall-Petch (HP) to inverse Hall-Petch (IHP) behaviors associated with grain size reduction has been recognized for over two decades. However, the underlying mechanisms for such transition in high entropy alloys (HEAs) under dynamic loading, in which abundant deformation mechanisms could be activated either sequentially or simultaneously, remain unclear. Here, we investigate the HP to IHP transition in nanocrystalline CoCrFeMnNi HEAs under shock loading by examining their deformation mechanisms and flow stresses using large-scale molecular dynamics (MD) simulations. It is found that this transition is strongly dependent on the shock pressure as a result of the complex interplay among multiple competing deformation mechanisms, including the hardening mechanisms such as dislocations interactions and grain boundary (GB) blocking, as well as the softening mechanisms like phase formation, amorphization, GB thickening, and grain rotation. Moreover, there exists a critical shock pressure, which corresponds to the largest critical grain size for the HP-IHP transition. Below the critical shock pressure, the critical grain size increases with pressure due to a stronger hardening effect in grain interior (GIs), while above the critical pressure, the critical grain size first decreases and then undergoes a pressure-insensitive plateau before further decrease due to softening effects in GIs. A theoretical model that includes different deformation mechanisms is proposed for the first time to capture the shock pressure-dependent HP-IHP transition. Our work provides valuable guidance for optimizing the grain size of nanocrystalline HEAs for applications involving dynamic loadings.

Enhancing surface strength of tungsten by gradient nano-grained structure

A gradient nano-grained (GNG) structure demonstrates satisfactory surface strength. However, the underlying mechanism responsible for its strengthening lacks sufficient research. To explain how gradient nano-grained structures improve surface strength in detail, large-scale parallel molecular dynamics simulations are utilized in this study to investigate the mechanical deformation behavior of BCC tungsten with varying grain sizes during spherical nanoindentation. The findings suggest that a well-designed gradient structure can promote rational plasticity and an appropriate distribution of internal atomic stress. The critical point of maximum stress and hardness is observed when the initial grain size is 4.5 nm, with an average grain size of 7.1 nm. The interaction between grain boundary slip and migration in small grains, along with the enhanced activity of grain boundary dislocations in large grains, collectively contributes to the enhancement of the strength and hardness of the GNG structure. Compared with a homogeneous nano-grained structure, the gradient nano-grained structure exhibits a more rational distribution of dislocations and stress relaxation effects to enhance strength. The present work utilizes the molecular dynamics nanoindentation method to study GNG materials, providing a methodology for investigating the surface strengthening effects of GNG structures at the atomic scale and effectively revealing potential mechanisms for resisting surface deformation in GNG structures.

Strain Engineering of Ion-Coordinated Nanochannels in Nanocellulose.

Expanding the interlayer spacing plays a significant role in improving the conductivity of a cellulose-based conductor. However, it remains a challenge to regulate the cellulose nanochannel expanded by ion coordination. Herein, starting from multiscale mechanics, we proposed a strain engineering method to regulate the interlayer spacing of the cellulose nanochannels. First-principles calculations were conducted to select the most suitable ions for coordination. Large-scale molecular dynamics simulations were performed to reveal the mechanism of interlayer spacing expansion by the ion cross-linking. Combining the shear-lag model, we established the relationship between interfacial cross-link density and interlayer spacing of an ion-coordinated cellulose nanochannel. Consequently, fast ion transport and current regulation were realized via the strain engineering of nanochannels, which provides a promising strategy for the current regulation of a cellulose-based conductor.

Low-index mesoscopic surface reconstructions of Au surfaces using Bayesian force fields

Metal surfaces have long been known to reconstruct, significantly influencing their structural and catalytic properties. Many key mechanistic aspects of these subtle transformations remain poorly understood due to limitations of previous simulation approaches. Using active learning of Bayesian machine-learned force fields trained from ab initio calculations, we enable large-scale molecular dynamics simulations to describe the thermodynamics and time evolution of the low-index mesoscopic surface reconstructions of Au (e.g., the Au(111)-‘Herringbone,’ Au(110)-(1 × 2)-‘Missing-Row,’ and Au(100)-‘Quasi-Hexagonal’ reconstructions). This capability yields direct atomistic understanding of the dynamic emergence of these surface states from their initial facets, providing previously inaccessible information such as nucleation kinetics and a complete mechanistic interpretation of reconstruction under the effects of strain and local deviations from the original stoichiometry. We successfully reproduce previous experimental observations of reconstructions on pristine surfaces and provide quantitative predictions of the emergence of spinodal decomposition and localized reconstruction in response to strain at non-ideal stoichiometries. A unified mechanistic explanation is presented of the kinetic and thermodynamic factors driving surface reconstruction. Furthermore, we study surface reconstructions on Au nanoparticles, where characteristic (111) and (100) reconstructions spontaneously appear on a variety of high-symmetry particle morphologies.

Orientation-dependent deformation mechanisms of alpha-uranium single crystals under shock compression

Large-scale non-equilibrium molecular dynamics (NEMD) simulations were employed to investigate the dynamic deformations of alpha-uranium (α-U) single crystals subjected to varying shock strengths along low-index crystallographic orientations. The pronounced anisotropy of α-U gives rise to a complex microstructural evolution under shock loading. In-depth microstructural analysis of post-shock specimens reveals the identification of multiple dynamic deformation mechanisms. Notably, when the shock loading direction aligns with the a-axis, dynamic deformation of the α-U single crystals is primarily dominated by lattice instability, which attributes to a crystalline-to-amorphous transition serving as the dominant shear stress relaxation pathway. On the other hand, shock loading along the b-axis results in an abundance of deformation twins, with twinning planes identified as (130) and (13¯0). During the twinning event, the α-U matrix undergoes a transition to a metastable intermediate phase, subsequently decomposing into a composite structure comprising α-U twins and matrix. This unconventional twinning mechanism significantly deviates from classical theories. Furthermore, upon loading along the c-axis, twinning and a phase transition from α-U to body-centered tetragonal phase (bct-U) occur in α-U single crystal samples. Given that the pressure threshold of this phase transition predicted by ab initio calculations is as high as ∼270 GPa, the phase transition from α-U to bct-U might be implausible. An alternative interatomic potential of uranium with the higher pressure threshold was employed to reinvestigate the shock response of α-U single crystals along the c-axis. The phase transition of α-U to bct-U disappears, and twinning dominates the plastic deformation, with the twinning orientation conforming to the {112} twinning. The strong anisotropy of the α-U lattice triggers a wealth of orientation-dependent dynamic deformation mechanisms. The activation of the twinning system is evidently associated with the loading direction, constituting the potential cause for the discovery of multiple twinning variants during the deformation in polycrystalline uranium.

Exploring the Interactions between two Ligands, UCB-J and UCB-F, and Synaptic Vesicle Glycoprotein 2 Isoforms.

In silico modeling was applied to study the efficiency of two ligands, namely, UCB-J and UCB-F, to bind to isoforms of the synaptic vesicle glycoprotein 2 (SV2) that are involved in the regulation of synaptic function in the nerve terminals, with the ultimate goal to understand the selectivity of the interaction between UCB-J and UCB-F to different isoforms of SV2. Docking and large-scale molecular dynamics simulations were carried out to unravel various binding patterns, types of interactions, and binding free energies, covering hydrogen bonding and nonspecific hydrophobic interactions, water bridge, π-π, and cation-π interactions. The overall preference for bonding types of UCB-J and UCB-F with particular residues in the protein pockets can be disclosed in detail. A unique interaction fingerprint, namely, hydrogen bonding with additional cation-π interaction with the pyridine moiety of UCB-J, could be established as an explanation for its high selectivity over the SV2 isoform A (SV2A). Other molecular details, primarily referring to the presence of π-π interactions and hydrogen bonding, could also be analyzed as sources of selectivity of the UCB-F tracer for the three isoforms. The simulations provide atomic details to support future development of new selective tracers targeting synaptic vesicle glycoproteins and their associated diseases.

Super-Suppression of Long-Wavelength Phonons in Constricted Nanoporous Geometries.

In a typical semiconductor material, the majority of the heat is carried by long-wavelength, long-mean-free-path phonons. Nanostructuring strategies to reduce thermal conductivity, a promising direction in the field of thermoelectrics, place scattering centers of size and spatial separation comparable to the mean free paths of the dominant phonons to selectively scatter them. The resultant thermal conductivity is in most cases well predicted using Matthiessen's rule. In general, however, long-wavelength phonons are not as effectively scattered as the rest of the phonon spectrum. In this work, using large-scale molecular-dynamics simulations, non-equilibrium Green's function simulations, and Monte Carlo simulations, we show that specific nanoporous geometries that create narrow constrictions in the passage of phonons lead to anticorrelated heat currents in the phonon spectrum. This effect results in super-suppression of long-wavelength phonons due to heat trapping and reductions in the thermal conductivity to values well below those predicted by Matthiessen's rule.

In Bacterial Membranes Lipid II Changes the Stability of Pores Formed by the Antimicrobial Peptide Nisin.

Resistance to available antibiotics poses a growing challenge to modern medicine, as this often disallows infections to be controlled. This problem can only be alleviated by the development of new drugs. Nisin, a natural lantibiotic with broad antimicrobial activity, has shown promise as a potential candidate for combating antibiotic-resistant bacteria. However, nisin is poorly soluble and barely stable at physiological pH, which despite attempts to address these issues through mutant design has restricted its use as an antibacterial drug. Therefore, gaining a deeper understanding of the antimicrobial effectiveness, which relies in part on its ability to form pores, is crucial for finding innovative ways to manage infections caused by resistant bacteria. Using large-scale molecular dynamics simulations, we find that the bacterial membrane-specific lipid II increases the stability of pores formed by nisin and that the interplay of nisin and lipid II reduces the overall integrity of bacterial membranes by changing the local thickness and viscosity.

Molecular Dynamics Simulations of Different Nanoparticles at Substrates.

We report the results of large-scale molecular dynamics simulations of adsorption nanoparticles on solid surfaces. The particles were modeled as stiff aggregates of spherical segments. Three types of particles were studied: rods, rectangles, and triangles built of the same number of segments. We show how the particle shape affects the adsorption, the structure of the surface layer, and the degree of the removal of particles from the solvent. The systems with different segment-segment and segment-surface interactions and different concentrations of particles were investigated. The ordered structures formed in adsorption monolayers were also analyzed. The results are consistent with experimental observations.

GENESIS CGDYN: large-scale coarse-grained MD simulation with dynamic load balancing for heterogeneous biomolecular systems.

Residue-level coarse-grained (CG) molecular dynamics (MD) simulation is widely used to investigate slow biological processes that involve multiple proteins, nucleic acids, and their complexes. Biomolecules in a large simulation system are distributed non-uniformly, limiting computational efficiency with conventional methods. Here, we develop a hierarchical domain decomposition scheme with dynamic load balancing for heterogeneous biomolecular systems to keep computational efficiency even after drastic changes in particle distribution. These schemes are applied to the dynamics of intrinsically disordered protein (IDP) droplets. During the fusion of two droplets, we find that the changes in droplet shape correlate with the mixing of IDP chains. Additionally, we simulate large systems with multiple IDP droplets, achieving simulation sizes comparable to those observed in microscopy. In our MD simulations, we directly observe Ostwald ripening, a phenomenon where small droplets dissolve and their molecules redeposit into larger droplets. These methods have been implemented in CGDYN of the GENESIS software, offering a tool for investigating mesoscopic biological processes using the residue-level CG models.

Turbulent spot formation in three-dimensional yukawa liquids using large-scale molecular dynamics simulation—effect of system size

We have performed classical ‘first principles’ 3D Molecular Dynamics (MD) simulation of plane-Couette flow (PCF) in a 3D Yukawa liquid. The main aim of the present investigation is to study the effect of stream-wise (flow-direction) and span-wise (transverse direction to the flow) width on the subcritical transition to turbulence in plane Couette flow (PCF), separately. In the past, taking very large-aspect ratio systems, subcritical transition to turbulence in PCF has been studied in great detail. However, the effect of stream-wise and span-wise width on the turbulent dynamics separately, have not been studied in detail. In the present work, we have investigated the effect of stream-wise and span-wise width or length and found that the stream-wise length enhances the large-scale energy, which gives rise to strong large-scale flow, and span-wise width enhances the small-scale energy, which gives rise to strong small-scale structures. In other words, topology of the turbulent spot is observed to change with the change in system size. The spectral separation between the modes governing the large and small-scale dynamics improves with the increase in system sizes. The connection between the stream-wise vortices and the stream-wise velocity streaks is investigated in details. We have found that the number stream-wise velocity streaks is one unit larger than the number of stream-wise vortices. A qualitative comparison between the results obtained from the MD simulation and the hydrodynamics experiment is presented in this article. The behavior of the system was observed to vary with the range of interactions among the dust grains.

Effects of shear strain on shock response in single crystal iron

With large-scale non-equilibrium molecular dynamics simulations and in situ x-ray diffraction analysis, we conducted a systematic investigation into the effects of pre-existing shear strain (γxy) on the shock response of single crystal iron. Our findings reveal significant effects of γxy on the deformation of the crystal structure during shock loading, leading to noticeable alterations in the propagation of shock waves. Specifically, during the elastic stage, the presence of γxy results in a reduction of shock strength, consequently diminishing the magnitude of elastic lattice strain (εe). In the plastic stage, γxy stimulates the α–ε phase transformation, and structure deformation undergoes a transition from the sequential activity of dislocation-to-transformation to the synchronous activity of dislocation and transformation. This transition inhibits the propagation of plastic waves and consequently broadens the elastic regime. Additionally, the introduction of γxy activates different slip systems, as it alters the corresponding resolved shear stress. Concurrently, the presence of γxy triggers the activation of different high-pressure phase variants. Our investigation sheds light on the fundamental physics of iron under shock compression and the influence of pre-existing shear strain on its behavior.

Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations

Reconstructive phase transitions involving breaking and reconstruction of primary chemical bonds are ubiquitous and important for many technological applications. In contrast to displacive phase transitions, the dynamics of reconstructive phase transitions are usually slow due to the large energy barrier. Nevertheless, the reconstructive phase transformation from β- to λ-Ti3O5 exhibits an ultrafast and reversible behavior. Despite extensive studies, the underlying microscopic mechanism remains unclear. Here, we discover a kinetically favorable in-plane nucleated layer-by-layer transformation mechanism through metadynamics and large-scale molecular dynamics simulations. This is enabled by developing an efficient machine learning potential with near first-principles accuracy through an on-the-fly active learning method and an advanced sampling technique. Our results reveal that the β−λ phase transformation initiates with the formation of two-dimensional nuclei in the ab-plane and then proceeds layer-by-layer through a multistep barrier-lowering kinetic process via intermediate metastable phases. Our work not only provides important insight into the ultrafast and reversible nature of the β−λ transition, but also presents useful strategies and methods for tackling other complex structural phase transitions.

Atomistic investigation of phase transformations in NiTiCu shape memory alloys

NiTiCu shape memory alloys have attracted much attention for their ability to reduce hysteresis, which is required fast responses in actuator and sensor applications, but the correlation between the properties and specific process of phase transformation is unclear. In this work, an atomic-level understanding of the detailed phase transformation behavior of NiTiCu shape-memory alloys is achieved based on a newly developed interatomic potential for the Ni–Ti–Cu ternary system. Free energy calculations based on the thermodynamic integration method faithfully reproduce reported experimental trends, such as the compositional dependency of different phase transformation paths. Further, large-scale molecular dynamics simulations performed on nanocrystalline alloys provide visual evidence of the detailed phase transformation process involving different martensitic transformation paths. Our results suggest that although both the composition and grain size can be adjusted to realize slim hysteresis, the effect of increasing the Cu amount is only significant if changes in the phase transformation modes are involved at relatively low Cu contents. When the material exhibits only a single phase transformation mode at relatively high Cu contents, the additional Cu is not highly effective in reducing the hysteresis.

Experimental High-Resolution Observation of the Truncated Double-Icosahedron Structure: A Stable Twinned Shell in Alloyed Au-Ag Core@Shell Nanoparticles.

Given the binary nature of nanoalloy systems, their properties are dependent on their size, shape, structure, composition, and chemical ordering. When energy and entropic factors for shapes and structure variations are considered in nanoparticle growth, the spectra of shapes become so vast that even metastable arrangements have been reported under ambient conditions. Experimental and theoretical variations of multiply twinned particles have been observed, from the Ino and Marks decahedra to polyicosahedra and polydecahedra with comparable energetic stability among them. Herein, we report the experimental production of a stable doubly truncated double-icosahedron structure (TdIh) in Au-Ag nanoparticles, in which a twinned Ag-rich alloyed shell is reconstructed on a Au-Ag alloyed Ino-decahedral core. The structure, chemical composition, and growth pathway are proposed on the basis of high-angle annular dark-field scanning transmission electron microscopy analysis and excess energy calculations, while its structural stability is estimated by large-scale atomic molecular dynamics simulations. This novel nanostructure differs from other structures previously reported.

Interface Amorphization Controls Maximum Wear Resistance of Multinanolayer DLC/WC Coatings.

Multilayer coatings offer significant advantages in protecting materials' surfaces by shielding the underlying materials hierarchically from damage and wear. The layering morphology and structure of multilayer coatings directly affect their wear resistance capacity. Using a systematic set of experiments and molecular dynamics (MD) simulations, we studied the effect of layering thickness on the macroscale wear response of DLC/WC multinanolayer coatings. Our study revealed the existence of a critical bilayer thickness where maximum scratch hardness and wear resistance can be achieved. Our large-scale MD simulations showed that reducing the WC layer thickness to a certain limit increases the scratch hardness due to the confinement of dislocation motion. However, when the thickness of the WC layers falls below 2 nm, the deformation mechanism transitions from the interface-induced dislocation confinement to the interface-mediated amorphization of WC layers, reducing the scratch hardness of the coating. This finding offers a procedure for optimizing the macroscale wear performance of multinanolayer coatings.

Design and evaluation of a multiepitope vaccine for pancreatic cancer using immune-dominant epitopes derived from the signature proteome in expression datasets.

Pancreatic cancer is a highly aggressive and often lethal malignancy with limited treatment options. Its late-stage diagnosis and resistance to conventional therapies make it a significant challenge in oncology. Immunotherapy, particularly cancer vaccines, has emerged as a promising avenue for treating pancreatic cancer. Multi-epitope vaccines, designed to target multiple epitopes derived from various antigens associated with pancreatic cancer, have gained attention as potential candidates for improving therapeutic outcomes. In this study, we have explored transcriptomics and protein expression databases to identify potential upregulated proteins in pancreatic cancer cells. After examining a total of 21,054 proteins from various databases, it was discovered that 143 proteins expressed differently in malignant and healthy cells. The CTL, HTL and BCE epitopes were predicted for the shortlisted proteins. 51,840 vaccine constructs were created by concatenating CTL, HTL, and B-cell epitopes in the respective sequences. The best 86 structures were selected from a set of 51,840 designs after they were analyzed for vaxijenicity, allergenicity, toxicity, and antigenicity scores. In further simulation of the immune response using constructs, it was found that 41417, 37961, and 40841 constructs could produce a strong immune response when injected. Further, it was found that construct 37961 showed stronger interaction and stability with TLR-9 as determined from the large-scale molecular dynamics simulations. Moreover, the 37961 construct has shown interactions with TLR-9 suggests its potential in inducing immune response. In addition, construct 37961 has shown 100% predicted solubility in the E. coli expression system. Overall, the study indicates the designed construct 37961 has the potential to induce an anti-tumor immune response and long-standing protection pending further studies.

Deep Potential fitting and mechanical properties study of MgAlSi alloy

MgAlSi alloy materials have the main properties of light weight and high strength, good electrical and thermal conductivity and corrosion resistance, and have various applications in the industrial field, making an important contribution to the realization of lightweight and high performance needs. In order to be able to predict the material properties of MgAlSi alloys with a high degree of accuracy, this paper develops for the first time an interatomic potential function for MgAlSi alloys based on a neural network machine learning approach. The effectiveness of the developed machine learning potentials is verified by analyzing the problems encountered during the training process and the errors of the finally obtained potential functions, and comparing some of the radial distribution functions, coordination numbers, and predictions of properties such as the equation of state, lattice constants, shear modulus and bulk modulus with those of AIMD. It is found that the performance error of the deep potential model is basically kept in the same order of magnitude as that of DFT calculations, the computational speed can be up to nearly a thousand times that of DFT, and the computational cost is linearly related to the atomic number, which is well suited for large-scale molecular dynamics simulations, and it will provide a promising solution for accurate large-scale molecular dynamics simulations.

Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials

We propose an efficient approach for simultaneous prediction of thermal and electronic transport properties in complex materials. Firstly, a highly efficient machine-learned neuroevolution potential (NEP) is trained using reference data from quantum-mechanical density-functional theory calculations. This trained potential is then applied in large-scale molecular dynamics simulations, enabling the generation of realistic structures and accurate characterization of thermal transport properties. In addition, molecular dynamics simulations of atoms and linear-scaling quantum transport calculations of electrons are coupled to account for the electron-phonon scattering and other disorders that affect the charge carriers governing the electronic transport properties. We demonstrate the usefulness of this unified approach by studying electronic transport in pristine graphene and thermoelectric transport properties of a graphene antidot lattice, with a general-purpose NEP developed for carbon systems based on an extensive dataset.