Interatomic Interactions Research Articles

Effect of bismuth on the microstructural evolution and properties of single-crystal Fe during rolling: a molecular dynamics study.

Bi-containing free-cutting steel has consistently attracted attention in the field of materials processing. The molecular dynamics (MD) simulations were used to investigate the impact of bismuth (Bi) nanoparticles on the internal structural evolution and mechanical properties of single-crystal Fe during the rolling process. The influence of varying rolling depths in the presence of nanoparticles is also examined. The results indicate that Bi nanoparticles significantly affect rolling force, stress distribution, and the generation and propagation of dislocations during nano-rolling. The deformation of Bi particles reduces both the rolling force and the overall von Mises stress in the Bi-affected region, while simultaneously hindering the generation and movement of dislocations, resulting in localized strengthening. As the rolling depth increases, the total rolling force, friction coefficient, and von Mises stress gradually rise. However, in the Bi particle region, the structural changes of Bi particles with increasing rolling depth lead to variable effects on normal rolling force, tangential friction force, and friction coefficient. The free-cutting alloy model was constructed using the molecular/atomic simulation software Atomsk. The entire simulation process was conducted using MD on the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) platform, with interatomic interactions described by classical force fields. The MD simulation time step was set to 1fs, and the temperature was maintained constant via the Nose-Hoover thermostat. Data post-processing and analysis were performed using the OVITO software.

Excitation threshold of solitons in anharmonic chains

Abstract This study numerically estimates the momentum threshold required to excite solitons in anharmonic chains. For both Fermi-Pasta-Ulam-Tsingou (FPUT)-αβ and FPUT-β chains, regardless of whether the interatomic interaction potential is symmetric, the required excitation momentum converges to the momentum of the soliton center (i.e., the peak momentum of the soliton) as the number of initially excited atoms increases. As the amplitude of the soliton approaches zero, the momentum threshold decreases to nearly zero, allowing soliton being excited with infinitesimal initial excitation momentum. These findings enhance the understanding of soliton dynamics and offer insights for optimizing soliton excitation methods, with potential applications in straintronics and nonlinear wave control technologies.

Hydrogenation Reaction Mechanisms on Ni-Doped MoS2 Catalysts: A Density Functional Theory Study of Sulfur Edge Engineering and Coregulated Electronic Effects.

Precise modulation of local interatomic interactions affecting the electronic structure is an important method to control the catalytic activity and reaction pathways. In this study, we focused on the hydrogenation reaction of naphthalene and employed density functional theory calculations to investigate the specific influence of electronic effects triggered by the coregulation of Ni and sulfur edge engineering on the hydrogenation performance of Ni-doped MoS2 at different edge sulfur coverages (Ni-MoS2-X-θs). Our findings reveal that the interaction between Ni and S in the catalyst matrix material modifies the local electronic structure surrounding the sulfur atoms in the active site. Notably, Ni doping facilitates significant electron transfer, altering the charge and the electronic states at the catalyst edge. This, in turn, affects the adsorption capacity and reactivity of the catalyst, thereby reducing the energy barrier of the hydrogenation reaction. Furthermore, we paid particular attention to the modulation of catalytic activity and reaction pathways under the Eley-Rideal (E-R) mechanism. Interestingly, the sulfur coverage exhibited a nonlinear relationship with the adsorption and activation properties of the probe molecule. Typically, changes in the Mo edge sulfur coverage probability of Ni-MoS2-X-θs have a greater impact on the activation properties. Through comprehensive studies, we demonstrated that both compositional and structural factors must be considered when tailoring the catalytic performance. Importantly, adjusting the ratio of marginal sulfur atoms to metal atoms to 1:1 can effectively enhance the catalytic activity. This study provides valuable insights into the electronic effects regulating the hydrogenation reaction activity of MoS2-based catalysts. It also opens the way for the rational design of novel hydrogenation catalysts, offering a new strategy for optimizing the catalytic performance.

Nonlocality and strength of interatomic interactions inducing the topological phonon phase transition

Understanding phonon behavior in semiconductors from a topological physics perspective offers opportunities to uncover extraordinary phenomena related to phonon transport and electron–phonon interactions. While various types of topological phonons have been reported in different crystalline solids, their microscopic origins remain quantitatively unexplored. In this study, analytical interatomic force constant (IFC) models are employed for wurtzite GaN and AlN to establish relationships between phonon topology and real-space IFCs. The results demonstrate that variations in the strength and nonlocality of IFCs can induce phonon phase transitions in GaN and AlN through band reversal, leading to the emergence of new Weyl phonons at the boundaries and within the Brillouin zones. Among the observed Weyl points, some remain identical in both materials under simple IFC modeling, while others exhibit variability depending on the specific case. Compared to the strength of the IFCs, nonlocal interactions have a significantly larger impact on inducing topological phonon phase transitions, particularly in scenarios modeled by the IFC model and the SW potential. The greater number of the third nearest neighbor atoms in wurtzite AlN provides more room for variations in the topological phonon phase than in GaN, resulting in more substantial changes in AlN.

Neural network potential for dislocation plasticity in ceramics

Dislocations in ceramics are increasingly recognized for their promising potential in applications such as toughening intrinsically brittle ceramics and tailoring functional properties. However, the atomistic simulation of dislocation plasticity in ceramics remains challenging due to the complex interatomic interactions characteristic of ceramics, which include a mix of ionic and covalent bonds, and highly distorted and extensive dislocation core structures within complex crystal structures. These complexities exceed the capabilities of empirical interatomic potentials. Therefore, constructing neural network potentials (NNPs) emerges as the optimal solution. Yet, creating a training dataset that includes dislocation structures proves difficult due to the complexity of their core configurations in ceramics and the computational demands of density functional theory for large atomic models containing dislocation cores. In this work, we propose a training dataset from properties that are easier to compute via high-throughput calculation. Using this dataset, we have successfully developed NNPs for dislocation plasticity in ceramics, specifically for three typical functional ceramics: ZnO, GaN, and SrTiO3. These NNPs effectively capture the nonstoichiometric and charged core structures and slip barriers of dislocations, as well as the long-range electrostatic interactions between charged dislocations. The effectiveness of this dataset was further validated by measuring the similarity and uncertainty across snapshots derived from large-scale simulations, alongside extensive validation across various properties. Utilizing the constructed NNPs, we examined dislocation plasticity in ceramics through nanopillar compression and nanoindentation, which demonstrated excellent agreement with experimental observations. This study provides an effective framework for constructing NNPs that enable the detailed atomistic modeling of dislocation plasticity, opening new avenues for exploring the plastic behavior of ceramics.

A discrete crystal model in three dimensions: The line-tension limit for dislocations

Abstract We propose a discrete lattice model of the energy of dislocations in three-dimensional crystals which properly accounts for lattice symmetry and geometry, arbitrary harmonic interatomic interactions, elastic deformations and discrete crystallographic slip on the full complement of slip systems of the crystal class. Under the assumption of diluteness, we show that the discrete energy converges, in the sense of Γ-convergence, to a line-tension energy defined on Volterra line dislocations, regarded as integral vector-valued currents supported on rectifiable curves. Remarkably, the line-tension limit is of the same form as that derived from semidiscrete models of linear elastic dislocations based on a core cutoff regularization. In particular, the line-tension energy follows from a cell relaxation and differs from the classical ansatz, which is quadratic in the Burgers vector.

2D MOF‐Based Filtration‐Sensing Strategy for Trace Gas Sensing Under Intense F‐Gas Interference at Room Temperature

AbstractThe detection of trace impurity gases in fluorinated gas (F‐gas) that are widely used in the industry offers a significant avenue for equipment status monitoring and mitigating unnecessary emissions. However, the formidable electron affinity (EA) and adsorption propensity of F‐gas molecules render the identification of trace impurities within a high‐concentration F‐gas atmosphere exceptionally challenging. Herein, the filtration‐sensing strategy is proposed to realize highly sensitive and selective Room Temperature (RT) sensing of trace gases in the F‐gas environment. Through the innovative construction of a bilayer structure, comprising Co3(HITP)2 as the overlayer and SnO2 nanofibers (NFs) as the sensing layer, remarkably sensitive detection of trace impurity gases under intense F‐gas interference conditions is achieved. The efficacy of the Co3(HITP)2 overlayer is further corroborated through the incorporation of Pd‐SnO2 and MoS2‐SnO2 sensors, concurrently facilitating targeted quantitative identification within a complex gas mixture environment. The underlying sensing mechanism is predominantly attributed to interatomic adsorption interactions and the modulation of gas diffusion by microporous structures. This work provides pioneering insights into trace impurity detection within high‐concentration F‐gas atmosphere while presenting a potentially viable solution for the operational maintenance of F‐gas‐based industrial equipment (F‐equipment) in industrial applications.

Study on grain refinement of AlB2 structure in aluminium alloys

ABSTRACT Aiming at the problem of unclear grain refinement mechanism in aluminium alloys, Al n B n (n = 2–12) clusters and Al–3B alloy were studied by density functional theory methods. The results show that as the cluster size increased, B atoms self-aggregated, and Al atoms surrounded the surface of the clusters. In addition, Al and B atoms formed stable AlB2 triangular structures. The average binding energy gradually increased with increasing clusters size, which indicated that the clusters structures were gradually stable. Range of n = 2–11, the Al9B9 cluster showed the most stable structure. Electronic properties and interatomic interactions calculation results suggested that there was a minimum electrostatic potential point on the surface of the B atom, and the B atom in AlB2 may be the adsorption site of α-Al. The ab initio molecular dynamics simulations results of the Al–3B alloy showed that Al and B generated the AlB2 structure in situ, and Al atoms nucleated and grew on the AlB2 surface. These results can be used to understand the grain refinement mechanism of the Al–B intermediate alloy.

Effective 1D-equation with a Lambert W function nonlinearity for cigar-shaped Bose–Einstein condensates

In this paper, we derive an effective 1D equation based on the work of Mateo and Delgado [Phys. Rev. A 77, 013617 (2008)] that governs the axial dynamics of mean-field cigar-shaped Bose–Einstein condensates (BECs) with repulsive interatomic interactions and subject to transverse anti-Gaussian confining potential. The resulting equation exhibits a nonstandard nonlinearity written in terms of the principal branch of the Lambert W function. This equation appropriately encompasses the correct limits within the Thomas–Fermi (TF) and perturbative regimes. The validity of our findings was established through a comparative analysis with numerical solutions derived from the complete three-dimensional Gross–Pitaevskii equation, including those about the limiting cases encountered in the TF and perturbative regimes.

Molecular dynamics study of the microstamping of TiAl6V4 alloy.

Microstamping has been shown to enhance the surface strength of alloy materials by improving interatomic density. This paper delves into the damage mechanism of TiAl6V4(TC4), which has been processed using high-speed stamping with varying overlap ratios at the atomic level. Additionally, the general trend of stress variation between loading and unloading is discussed. The mechanical properties of the substrate and the changes in microstructure resulting from varying overlap rates in microstamping were investigated. The impact of different machining overlap ratios on the depth of the damaged layer, the number of dislocation density lines, and the density of the matrix is also explored. The results indicate that the dislocation density remains relatively unchanged due to material hardening, while the overlap ratio increases continuously. Based on this analysis, a more optimal microstamping overlay ratio parameter is proposed to effectively enhance the surface strength of the substrate and reduce processing time. First, an alloy model with titanium, aluminum, and vanadium was created in ATOMSK and LAMMPS software. The model was divided into three layers: fixed, constant temperature, and Newton. To ensure the accuracy of the simulation, the system was annealed in order to minimize energy and replicate real-world conditions. Zhou's EAM alloy potential was employed to represent the interaction between the alloy atoms, while the Tersoff potential was used to represent the interatomic interaction of the diamond indenter. Additionally, the LJ potential function was selected to depict the interaction between the metal atom and the diamond indenter. The construct surface mesh method in OVITO software was then utilized to construct a surface mesh and analyze the impact of different machining overlap rates on surface topography. The common neighborhood analysis (CNA) module in OVITO was used to calculate the number of defective atoms and the depth of the damaged layer. Finally, the DXA (dislocation extraction analysis) module in OVITO was used to calculate the dislocation density length and dislocation density.

Exploring structural variances in monatomic metallic glasses using machine learning and molecular dynamics simulation.

BCC and FCC metals have different glass-forming abilities (GFA) and exhibit different characteristics during the glass transition. However, the structural origin of their different GFAs is still not clear. Here, we explored the structures of eight monatomic metallic glasses by combining molecular dynamics (MD) simulations and machine learning (ML). Our findings reveal that, despite their common long-range disordered atomic structure, metallic glasses can be further classified into two distinct categories indicating an underlying structural order within the disorder. Using machine learning, we found that BCC liquids can sample more diverse glass states than FCC liquids. Furthermore, glasses formed from BCC metals (GFFBs) exhibit a higher degree of disorder than glasses formed from FCC metals (GFFFs). These findings highlight the inherent differences between GFFFs and GFFBs, which help explain the different glass-forming abilities of FCC and BCC metals. Additionally, our results demonstrate the promising potential of computer vision and ML methods in exploring material structures. Classical molecular dynamics simulations were employed to generate configurations of GFFBs and GFFFs, and the simulations were performed using the LAMMPS code. Inter-atomic interactions were described using a classical embedded atom model (EAM) potential. The initial configuration of the model consists of 32,000 atoms in a three-dimensional (3D) cubic box with periodic boundary conditions applied in all three directions. For machine learning, we utilized an unsupervised machine learning method along with MobileNetV2 for classifying glass structures. Image entropy and image distances were used to measure the structural differences of the metallic glasses.

Transfer of solitons and half-vortex solitons via adiabatic passage

We show that the transfer of matter-wave solitons and half-vortex solitons in a spin-orbit coupled Bose-Einstein condensate between two (or more) arbitrarily chosen sites of an optical lattice can be implemented using the adiabatic passage. The underlying linear Hamiltonian has a flat band in its spectrum, so that even sufficiently weak interatomic interactions can sustain well-localized Wannier solitons which are involved in the transfer process. The adiabatic passage is assisted by properly chosen spatial and temporal modulations of the Rabi frequency. Within the framework of a few-mode approximation, the mechanism is enabled by a dark state created by coupling the initial and target low-energy solitons with a high-energy extended Bloch state, like in the conventional stimulated Raman adiabatic passage used for the coherent control of quantum states. In real space, however, the atomic transfer between initial and target states is sustained by the current carried by the extended Bloch state, which remains populated during the whole process. The full description of the transfer is provided by the Gross-Pitaevskii equation. Protocols for the adiabatic passage are described for one- and two-dimensional optical lattices, as well as for splitting and subsequent transfer of an initial wave packet simultaneously to two different target locations. Published by the American Physical Society 2024

Assessment of the free shear boundary condition in a capillary meniscus via molecular dynamics

Computational fluid dynamics models often employ the free shear boundary condition at free surfaces, a result from the continuity of the stress and the large viscosity contrast at liquid–gas interfaces. This study leverages nonequilibrium molecular dynamics simulations to investigate the validity of the free shear boundary condition on the exposed surface of a liquid meniscus at the nanoscale. The primary objective is elucidating the fundamental mechanisms and behavior of fluid interactions within a capillary meniscus formed between two carbon nanotubes (CNTs) in shear-driven flow. Shear-driven flow simulations were conducted by varying the velocity of a solid slab to induce different shear rates in the adjacent water molecules. The results demonstrate, for the first time, negligible shear at the free surface, supporting the free shear assumption from the nanoscale point of view. A force balance analysis reveals that capillary and surface tension forces dominate within the meniscus, dictating its shape and stability. Meniscus deformation was observed and primarily attributed to interatomic interactions between water molecules and CNTs, driven by a combination of short-range repulsive forces and van der Waals attractions. The minimal contribution from shear forces suggests that interatomic forces, rather than applied shear stress, are the primary drivers of the meniscus deformation. These findings offer valuable insights into fluid behavior and a sound fundamental analysis of the free shear boundary condition at the nanoscale.

Exact average many-body interatomic interaction model for random alloys

Understanding the physical origin of mechanisms in random alloys that lead to the formation of microstructures requires an understanding of their average behavior and, equally important, the role of local fluctuations around the average. Material properties of random alloys can be computed using direct simulations on random configurations. However, some properties are very difficult to compute, for others it is not even fully understood how to compute them using random sampling, in particular, interaction energies between multiple defects. To that end, we develop an atomistic model that does the averaging on the level of interatomic potentials. With such an average interatomic interaction model the problem of averaging via random sampling is bypassed since the problem of computing material properties on random configurations reduces to the problem of computing material properties on single crystals, the average alloy, which can be done using standard techniques.To be predictive, we develop our average model on the class of linear machine-learning interatomic potentials (MLIPs). To that end, using tools from higher-order statistics, we derive an analytic expansion of average many-body per-atom energies of linear MLIPs in terms of average tensor products of the feature vectors of the underlying machine-learning model that scales linearly with the size of an atomic neighborhood. In order to avoid forming higher-order tensors composed of products of feature vectors, we develop an implementation using equivariant tensor network (ETN) potentials, a class of linear MLIPs, that contracts the feature vectors to small-sized tensors, and then takes the average. We validate the average ETN potential by demonstrating the convergence of direct Monte Carlo simulations to the exact value for properties of the NbMoTaW medium-entropy alloy. Simulating average properties of dislocations has thus far only been possible with average embedded atom method potentials that, however, predict artificial polarized cores. Here, we show that average ETN potentials predict the compact screw dislocation core structure, in agreement with density functional theory. Hence, we anticipate that our model will become useful for understanding mechanistic origins of material properties and for developing predictive models of mechanical properties of random alloys.

Molecular dynamics study of the scratching property of the tetrahedral amorphous carbon coating on the piston ring

In order to reduce friction and wear of the piston ring-cylinder liner system of the internal combustion engine, the tetrahedral amorphous carbon (ta-C) coating on the piston ring is considered. The scratching properties of the ta-C coating and diamond ball are studied by molecular dynamics simulation. The atomic configuration, surface morphology, RMS roughness, friction force, coefficient of friction, wear rate, and normal stress of the ta-C coating are analysed under different scratching depths, substrate temperatures, and scratching velocities. The interatomic interactions are described using the second nearest-neighbour modified embedded-atom method potential, as well as Tersoff potential and Lennard-Jones potential. The simulation results show that the ta-C coating exhibits excellent scratching properties. The scratching depth has a significant effect on the scratching property of the ta-C coating, while the substrate temperature and scratching velocity have a relatively small effect.

Exceptionally Short Tetracoordinated Carbon-Halogen Bonds in Hexafluorodihalocubanes.

Molecules that contain bonds whose length significantly deviates from the average are of interest in the context of understanding the nature and limits of the chemical bonds. However, it is difficult to disentangle the individual contributions of the multiple factors that give rise to such bond-length deviations as reports on such molecules remain scarce. In the present study, we have succeeded in synthesizing hexafluorodihalocubanes of the type C8F6X2 (2) (X = Cl (2Cl), Br (2Br), I (2I)), which represent a new series of molecules with unusual C(sp3)-halogen bonds. The C(sp3)-halogen bonds of 2Cl, 2Br, and 2I, determined via single-crystal X-ray diffraction analysis, are approximately 0.07-0.09 Å shorter than typical C(sp3)-halogen bonds. In particular, the carbon-iodine bonds of 2I are the shortest C(sp3)-I bonds reported to date. The solution-state structures and electronic states of the C(sp3)-halogen bonds in these hexafluorodihalocubanes were analyzed by X-ray absorption spectroscopy, which revealed detailed information on the length of these C(sp3)-halogen bonds in solution and the solid state as well as on the electron-deficient nature of 2. Detailed theoretical calculations and a comparison with halotrinitromethanes (1), which represent another series of molecules with shortened C(sp3)-halogen bonds, revealed that the factors responsible for the shortening of the C(sp3)-halogen bond vary among the different C(sp3)-halogen bonds, i.e., for C(sp3)-Cl and C(sp3)-Br, the s-character and hyperconjugation effects predominate, whereas for C(sp3)-I, the interatomic Coulombic interaction effect prevails.

The temperature evolution of the atomic structure and the influence of the local environment of atoms on the optical properties of the NA2SIF6 crystal

Crystals of sodium hexafluorosilicate Na2SiF6 millimeter size were grown by the hydrothermal method. According to X-ray diffraction analysis, it was revealed that Na2SiF6 samples are twinned according to the merohedral law and crystallize in sp. gr. P321 with unit cell parameters equal at 295 K a = 8.8582(12), c = 5.0396(11) Å, V = 342.47(17) Å3 on average the results of repeated measurements. A multi-temperature diffraction study of Na2SiF6 was carried out, based on the results of which the temperature dynamics of the optical properties of crystals was calculated. The structural similarity of Na2SiF6 crystals with crystals of the langasite family La3Ga5SiO14 was found. This made it possible to explain the optical activity of Na2SiF6 by considering electron density spirals similar to langasite, twisted around a triple axis of symmetry passing through the origin of the Na2SiF6 cell. The fractures in the temperature dependences of the refractive indices and rotation of the plane of polarization of light are explained by taking into account the anomalous features of interatomic interactions along the triple axis of the crystal cell passing through the Si2(2d) position with coordinates (1/3, 2/3, z). It was found that the main factor influencing the temperature dynamics of optical parameters is the Si2(2d)–F2(6g) distance, which increases abnormally with cooling.

Predicting viscosity-concentration dependencies of binary organic mixtures using molecular dynamics methods

The shear viscosity of organic liquids is very important for industrial applications. This paper focuses on the blind prediction of concentration-viscosity dependencies for organic mixtures at 298.15 K and 1 bar using molecular dynamics methods. Two mixtures are considered: tributyrin+1-decanol and 1,2-butanediol+1-decanol. The interatomic interactions are described using the COMPASS force field, which is modified for the reproduction of pure compound viscosities. The Green–Kubo method is used to calculate the shear viscosities. Our approach provides accurate predictions for the viscosities of mixtures with a relative mean absolute error below 10%. This work is devoted to the participation in the 12th Industrial Fluid Properties Simulation Challenge.

Long-Lived Magnetization in an Atomic Spin Chain Tuned to a Diabolic Point.

Scaling magnets down to where quantum size effects become prominent triggers quantum tunneling of magnetization (QTM), profoundly influencing magnetization dynamics. Measuring magnetization switching in an Fe atomic chain under a carefully tuned transverse magnetic field, we observe a nonmonotonic variation of magnetization lifetimes around a level crossing, known as the diabolic point (DP). Near DPs, local environment effects causing QTM are efficiently suppressed, enhancing lifetimes by three orders of magnitude. Adjusting interatomic interactions further facilitates multiple DPs. Our Letter provides a deeper understanding of quantum dynamics near DPs and enhances our ability to engineer a quantum magnet.

Fast prediction of anharmonic vibrational spectra for complex organic molecules

Interpreting Raman and IR vibrational spectra in complex organic molecules lacking symmetries poses a formidable challenge. In this study, we propose an innovative approach for simulating vibrational spectra and attributing observed peaks to molecular motions, even when highly anharmonic, without the need for computationally expensive ab initio calculations. Our approach stems from the time-dependent stochastic self-consistent harmonic approximation to capture quantum nuclear fluctuations in atom dynamics while describing interatomic interaction through state-of-the-art reactive machine-learning force fields. Finally, we employ an isotropic charge model and a bond capacitor model trained on ab initio data to predict the intensity of IR and Raman signals.