Empirical Interatomic Potentials Research Articles

Modeling of the adsorption of a protein-fragment on kaolinite with potential antiviral activity

This work aimed at studying the potentiality of interactions between kaolinite surfaces and a protein-fragment (350–370 amino acid units) extracted from the glycoprotein E1 in the transmembrane domain (TMD) of hepatitis C virus capsid. A computational work was performed for locating the potential electrostatic interaction sites between kaolinite aluminol and siloxane surfaces and the residues of this protein-fragment ligand, monitoring the possible conformational changes. This hydrated neutralized kaolinite/protein-fragment system was simulated by means of molecular modeling based on atomistic force fields based on empirical interatomic potentials and molecular dynamic (MD) simulations. The MD calculations indicated that the studied protein-fragment interacted with the kaolinite surfaces with an exothermic process and structural distortions were observed, particularly with the hydrophilic aluminol surface by favorable adsorption energy. The viral units isolation or trapping by the adsorption on the kaolinite nanoparticles producing structural distortion of the peptide ligands could lead to the blockage of the entry on the receptor and hence a lack of viral activity would be produced. Therefore, these findings with the proposed insights could be an useful information for the next experimental and development studies in the area of discovering inhibitors of the global challenged hepatitis and other pathogenic viruses based on the phyllosilicate surface activity. These MD studies can be extended to other viruses like the COVID-19 interacting with silicate minerals surfaces.

Assessment of empirical interatomic potential to predict thermal conductivity in ThO2 and UO2

Assessment of empirical interatomic potential to predict thermal conductivity in ThO2 and UO2

A Simple Approach to Growth Mode of InN and InGaN Thin Films on GaN(0001) Substrate

Group-III nitride semiconductors have attracted much attention for application in optoelectronic devices such as light emitting diodes and laser diodes. In particular, highly efficient light emitting devices using self-assembled quantum dots have been pained much attention. For InN and InGaN grown on GaN(0001) substrate, it has been reported that three-dimensional (3D) islands are formed after two-dimensional (2D) wetting layers, resulting in Stranski-Krastanow (SK) growth mode [1,2]. Moreover it has been reported that the growth modes of InN on GaN(0001) substrate during molecular beam epitaxy depend on growth conditions such as V/III ratio and substrate temperature[3]: N-rich condition favors 3D growth, whereas In-rich condition leads to 2D growth. However, it is still unclear what is the crucial factor for determining the growth modes of InN and InGaN depending on the growth condition. In this study, the growth mode of InN and InGaN thin films on GaN(0001) substrate are investigated using the macroscopic theory using free energy formula [4] with the aid of empirical potential calculations [5] as well as ab initio calculations [6]. Effects of pyramids island with {1-103} facets and misfit dislocation (MD) formation are taken into account for the evaluation of free energies of InN and InGaN thin films on GaN(0001) substrate.The calculations of cohesive energy difference ΔE as a function of InN film thickness for 2D coherent growth (2D-coherent) and 2D growth with MD (2D-MD) using empirical interatomic potentials reveals that 2D-MD is more stable than 2D-coherent for InN films beyond 0.7 monolayers (ML). On the basis of these results, the MD formation energy Ed and the effective elastic constant M are estimated to be 1.31 eV/Å and 5.60×1011 N/m2. The calculated result of Ed reasonably agrees with previous calculations for dislocation core energy in bulk InN (1.51 eV/Å) [7]. Moreover, the analysis of free energies using the value of Ed and M and surface energies obtained by ab initio calculations (~ 0.074 eV/ Å2) demonstrate that the critical thickness between growth 2D-coherent and 2D-MD under In-rich conditions is 0.45 ML. The calculated results are qualitatively consistent with the experimental results [3]. The calculated phase diagram for growth mode as functions of surface energy of (0001) and surface energy difference between (0001) and (1-103) planes suggest that growth mode boundary between 2D-MD and 3D coherent growth with hexagonal pyramid island formation (3D-coherent) strongly depends on the surface energy difference between (0001) and (1-103) planes. 2D-MD is favorable when the surface energy of InN(1-103) is larger than that of InN(0001), whereas 3D-coherent is favorable when the surface energies of InN(1-103) is smaller than InN(0001). These results imply that the relative stability between (0001) and (1-103) planes depending on the growth condition is decisive for the growth modes of InN on GaN(0001) substrate.[1] S. Ploch et al., Phys. Status. Solidi C 6, 574 (2009). [2] C. Adelmann et al., Appl. Phys. Lett. 76, 1570 (2000). [3] Y. F. Ng et al., Appl. Phys. Lett. 81, 3960 (2002). [4] K. Shiraishi et al., J. Cryst. Growth 237, 206 (2002). [5] K. E. Khor, and S. Das Sarma, Phys. Rev. B 38, 3318 (1988). [6] T. Akiyama et al., Phys. Status Solidi B 255, 1700329 (2018). [7] T. Ito et al., J. Cryst. Growth 298, 186 (2007).Figure: Phase diagrams of growth modes boundary between 2D-MD and 3D-coherent of InN on GaN(0001) substrate as functions of surface energy of (0001) and surface energy difference between (0001) and (1-103) planes. Figure 1

Molecular interactions of praziquantel drug with nanosurfaces of sepiolite and montmorillonite

Praziquantel drug is nowadays in continuo study to overcome adverse biopharmaceutical properties as drug of first choice for the treatment of the human schistosomiasis. Meanwhile, sepiolite and montmorillonite have been studied experimentally as low-cost nanocarrier to design new praziquantel delivery systems. In this work, the interactions between the drug and clay minerals surfaces are studied at molecular level by computational methods. The optimization of the adsorption of praziquantel in sepiolite nanochannels and in the montmorillonite interlayer space has been studied by using force fields based on empirical interatomic potentials and at quantum mechanical level. The results showed the main interactions between the drug and both clay minerals surfaces at the confined spaces of these minerals, and favorable adsorption energies of the praziquantel-clays complexes were obtained. Therefore, theoretical calculations allow knowing how the drug is adsorbed in the clays and in the case of the montmorillonite how many molecules of the drug are adsorbed in the interlayer space of the mineral according to the experimental data.

Assessing the EDIP potential for atomic simulation of carbon diffusion, segregation and solubility in silicon melt

Molecular simulation based on empirical interatomic potential is an important tool in the study of impurity thermophysical properties, e.g. carbon diffusion, segregation and solubility in liquid silicon, which are key factors determining the carbon concentration and distribution during silicon crystal growth. The Tersoff potentials, although being very successful in describing many silicon and carbon properties, overestimate the silicon crystal melting temperature severely which incurs many criticism and restrictions when comparing the simulation results with experiments. In this study, the EDIP potential, which predicts an experimental comparable silicon melting temperature, was assessed whether it can reasonably capture the carbon diffusivity, segregation and solubility properties in silicon melt while avoiding the well-known temperature issue. We found that the EDIP potential, without requiring scaling the temperature, predicts consistent carbon and silicon diffusion coefficients with other potentials. The segregation coefficient estimated by EDIP is slightly higher than the experimental results. It fails in capturing the carbon solubility property by overestimating nearly two orders of magnitude higher than the experimental values. Based on potential energy and entropy calculation, we suggest that the problem of the EDIP potential is caused by the high entropy of the silicon melt phase. Overall, the research will provide useful information for other thermophysical studies based on EDIP potential and also be benefit to empirical potential optimization.

Molecular Dynamics Study of Thermal Conductivity of Silver Chalcogenides

To obtain an accurate thermal conductivity of Ag2Se considering heat‐flux effects, computational requirements such as simulation times and number of atoms are investigated for two methods: the Green–Kubo (GK) formula in equilibrium molecular dynamics (EMD) simulations and perturbed MD (pMD). An empirical interatomic potential is used, which reproduces both the superionic and nonsuperionic phases of Ag2Se. It is found that the accurate thermal conductivity based on the GK formula requires at least the correlation time of 12 ps, the simulation time of 24 ns, and the number of atoms of 3000. In contrast, for the pMD, the simulation time of ≈1.2 ns is needed to obtain a comparable thermal conductivity. It is also confirmed that the heat flux must be considered in MD simulations to qualitatively reproduce the temperature dependence of thermal conductivity observed experimentally.

Computational Modelling Studies of Structural Transition in Li1+XMn2O4 (0≤x≤1) Nanoparticles

Lithium-ion batteries, comprising nanoparticulate Ni-Mn-Co (NMC) cathodes that have been used to power electric vehicles, can be improved by blending NMC with LiMn2O4 (LMO). However, LMO undergoes a cubic to tetragonal phase change during charge cycling, which cracks and pulverises the material resulting in capacity fading [1]. Structural characterisation during the phase transition is the first step in mitigating capacity fading and can be challenging experimentally and computationally. The simulated amorphisation and recrystallisation method, based on empirical interatomic potentials were utilised to generate Li-Mn-O composite nanoparticles, with approximately 3000 atoms, and are amnenable to linear scaling DFT methods. The techniques was previously successfully used to study nanostructures of MnO2 [2], with composite structures. A wealth of microstructural features such as heterostructures, point defects and grain boundaries were observed. The discharge process of the nanoparticle was simulated by lithiating the Li-Mn-O composite nanoparticle. The nanoparticle was characterized by radial distribution functions and simulated X-ray diffraction patterns (XRDs). Furthermore, the structural changes with lithiation were visualised and details of various defects, including grain boundaries could be observed from microstructures. The presence of layered and spinel components were also noted and validated by XRDs. Linear scaling density functional theory methods were used to determine voltage profiles of simulated structures.

Learning to Use the Force: Fitting Repulsive Potentials in Density-Functional Tight-Binding with Gaussian Process Regression.

The Density-Functional Tight Binding (DFTB) method is a popular semiempirical approximation to Density Functional Theory (DFT). In many cases, DFTB can provide comparable accuracy to DFT at a fraction of the cost, enabling simulations on length and time scales that are unfeasible with first-principles DFT. At the same time (and in contrast to empirical interatomic potentials and force fields), DFTB still offers direct access to electronic properties such as the band structure. These advantages come at the cost of introducing empirical parameters to the method, leading to a reduced transferability compared to true first-principle approaches. Consequently, it would be very useful if the parameter sets could be routinely adjusted for a given project. While fairly robust and transferable parametrization workflows exist for the electronic structure part of DFTB, the so-called repulsive potential Vrep poses a major challenge. In this paper, we propose a machine-learning (ML) approach to fitting Vrep, using Gaussian Process Regression (GPR) to reconstruct Vrep with DFT-DFTB force residues as training data. The use of GPR circumvents the need for nonlinear or global parameter optimization, while at the same time offering arbitrary flexibility in terms of the functional form. We also show that the proposed method can be applied to multiple elements at once, by fitting repulsive potentials for organic molecules containing carbon, hydrogen, and oxygen. Overall, the new approach removes focus from the choice of functional form and parametrization procedure, in favor of a data-driven philosophy.

Neural-network interatomic potential for grain boundary structures and their energetics in silicon

Artificial neural-network (ANN) interatomic potentials for simulating atomic structures and energetics of grain boundaries (GBs) in silicon were constructed and integrated into structural optimization and molecular dynamics (MD) algorithms. A training dataset including various atomic environments of symmetric tilt GBs was generated by performing density-functional-theory (DFT) calculations. The ANN potential after training was found to be capable of approximating the potential-energy surface at GBs even with dangling bonds and large atomic displacements at high temperatures, which cannot be well reproduced with empirical interatomic potentials. Additionally, reliability of the ANN potential for molecular simulations was evaluated. GB structures optimized or equilibrated by the ANN molecular simulations were also energetically lower for DFT calculations, without significant errors. The ANN potential is therefore expected to greatly reduce structural-optimization iterations and required time steps to acquire stable or equilibrium GB structures in MD simulations, enabling us to address even large-scale systems of general GBs in silicon, with high accuracy and low computational cost.

Grain boundary structure in high-entropy alloys

We report atomistic simulation studies of grain boundary structure in a high-entropy FCC alloy. The simulations are based on empirical interatomic potentials and use massively parallel molecular dynamics techniques at the atomistic level to study the local structure. The studies address a series of pure tilt grain boundaries with random misorientations around the [110] crystallographic axis. We study the relaxed structures using various models and visualization techniques, including analyzing the dislocation content of the boundary region. A main focus is the role that the local composition in the random alloy plays in the structure and energy of the boundaries. This is performed by comparing the structures obtained for the complex random alloy with a corresponding “average atom” material that has the same average properties, but no local randomness. The implications for the overall properties of high-entropy alloys are discussed.

Adsorption of tamoxifen on montmorillonite surface

Abstract The anticancerous drug tamoxifen is becoming a promising therapy, although some administration handicaps should be improved. Montmorillonite is a natural phyllosilicate and its use as carrier of the tamoxifen can be an interesting alternative for drug delivery system. Syntheses at different pH of montmorillonite-tamoxifen hybrids have shown promising possibilities. However, it is necessary to know the driving forces and intermolecular interactions responsible for tamoxifen adsorption on montmorillonite at different pH. Atomistic calculations of these systems have been applied based on empirical interatomic potentials. Theoretical simulations were used to explain the stoichiometry of the tamoxifen intercalation into the montmorillonite to understand the experimental behavior. The results showed there is influence on the clay structure when there is pH adjustment, with varying particle size, as well as variability in drug intercalation. Cationic species of tamoxifen was adsorbed by cation exchange mechanism and an additional adsorption of molecule as tamoxifen citrate pair also contributed in the interaction, being more energetically favorable.

Accurate Prediction of Nanovoid Structures and Energetics in Bcc Metals

Knowledge on structures and energetics of nanovoids is fundamental to understand defect evolution in metals. Yet there remain no reliable methods able to determine essential structural details or to provide accurate assessment of energetics for general nanovoids. Here, we performed systematic first-principles investigations to examine stable structures and energetics of nanovoids in bcc metals, explicitly demonstrated the stable structures can be precisely determined by minimizing their Wigner-Seitz area, and revealed a linear relationship between formation energy and Wigner-Seitz area of nanovoids. We further developed a new physics-based model to accurately predict stable structures and energetics for arbitrary-sized nanovoids. This model was well validated by first-principles calculations and recent nanovoid annealing experiments, and showed distinct advantages over the widely used spherical approximation. The present work offers mechanistic insights that crucial for understanding nanovoid formation and evolution, provides crucial benchmarks for assessing empirical interatomic potentials, and enables a critical step towards predictive control and prevention of nanovoid related damage processes in structural metals.

A minimal Tersoff potential for diamond silicon with improved descriptions of elastic and phonon transport properties

Silicon is an important material and many empirical interatomic potentials have been developed for atomistic simulations of it. Among them, the Tersoff potential and its variants are the most popular ones. However, all the existing Tersoff-like potentials fail to reproduce the experimentally measured thermal conductivity of diamond silicon. Here we propose a modified Tersoff potential and develop an efficient open source code called GPUGA (graphics processing units genetic algorithm) based on the genetic algorithm and use it to fit the potential parameters against energy, virial and force data from quantum density functional theory calculations. This potential, which is implemented in the efficient open source GPUMD (graphics processing units molecular dynamics) code, gives significantly improved descriptions of the thermal conductivity and phonon dispersion of diamond silicon as compared to previous Tersoff potentials and at the same time well reproduces the elastic constants. Furthermore, we find that quantum effects on the thermal conductivity of diamond silicon at room temperature are non-negligible but small: using classical statistics underestimates the thermal conductivity by about 10% as compared to using quantum statistics.

Empirical interatomic potential for Fe-N binary system based on Finnis–Sinclair potential

A new empirical potential for the Fe-N binary system based on the Finnis–Sinclair (FS) potential was developed in order to analyze the strengthening mechanism of N addition in ferritic and austenitic steels. The potential parameters for the Fe-N and N-N interactions were determined to reproduce the physical properties of the first principles calculation data of four iron nitrides (ZnS type-FeN, ε- Fe3N, γ′- Fe4N and α′′- Fe16N2) obtained in this study. Furthermore, we investigated the reproducibility of physical properties of the other nitrides (NaCl type-FeN, ε- Fe2N and ζ- Fe2N) and the N behavior in body-centered cubic (bcc) and face-centered cubic (fcc) iron. The results showed good agreement with reference data.

Structural and elastic properties of amorphous carbon from simulated quenching at low rates

We generate representative structural models of amorphous carbon (a-C) from constant-volume quenching from the liquid with subsequent relaxation of internal stresses in molecular dynamics simulations using empirical and machine-learning interatomic potentials. By varying volume and quench rate we generate structures with a range of density and amorphous morphologies. We find that all a-C samples show a universal relationship between hybridization, bulk modulus and density despite having distinctly different cohesive energies. Differences in cohesive energy are traced back to slight changes in the distribution of bond-angles that is likely linked to thermal stability of these structures.

Guidelines for creating artificial neural network empirical interatomic potential from first-principles molecular dynamics data under specific conditions and its application to α-Ag2Se.

First-principles molecular dynamics (FPMD) simulations are highly accurate, but due to their high calculation cost, the computational scale is often limited to hundreds of atoms and few picoseconds under specific temperature and pressure conditions. We present here the guidelines for creating artificial neural network empirical interatomic potential (ANN potential) trained with such a limited FPMD data, which can perform long time scale MD simulations at least under the same conditions. The FPMD data for training are prepared on the basis of the convergence of radial distribution function [g(r)]. While training the ANN using total energy and atomic forces of the FPMD data, the error of pressure is also monitored and minimized. To create further robust potential, we add a small amount of FPMD data to reproduce the interaction between two atoms that are close to each other. ANN potentials for α-Ag2Se were created as an application example, and it has been confirmed that not only g(r) and mean square displacements but also the specific heat requiring a long time scale simulation matched the FPMD and the experimental values. In addition, the MD simulation using the ANN potential achieved over 104 acceleration over the FPMD one. The guidelines proposed here mitigate the creation difficulty of the ANN potential, and a lot of FPMD data sleeping on the hard disk after the research may be put on the front stage again.

Finite element analysis and molecular dynamics simulations of nanoscale crack-hole interactions in chiral graphene nanoribbons

Nanoscale defects (such as cracks, holes) often occur in graphene nanoribbons (GNRs). However, it is still a big challenge to accurately predict crack-hole interactions in them. In this study, the nanocale crack-hole interactions in chiral GNRs are investigated under mode-I loading using molecular dynamics (MD) simulations and finite element (FE) analysis. The carbon-carbon (CC) bond in the FE method is modeled as a nonlinear Timoshenko beam based on the full-atom Reactive Empirical Bond-Order interatomic potential of second generation (REBO potential) for the first time. The present MD and FE results show that the shielding effects on the crack tip stress field are dominated by the angle is θ, the hole-to-crack tip spacing r and the chirality of the GNRs. Checking against the linear-elastic fracture mechanics (LEFM) predictions of some crack-hole configurations shows that the present FE method and MD simulations have high accuracy. This study should be of great help for understanding nanoscale crack-hole interactions in GNRs and providing physical insights into the origins of defect engineering of GNRs.

Effect of surface reconstructions on misfit dislocation formation in InAs/GaAs(001)

The effect of surface reconstructions on misfit dislocation (MD) formation in InAs/GaAs(001) is theoretically investigated using empirical interatomic potentials. According to the calculated cohesive energy using empirical interatomic potentials, in the case of InAs/GaAs(001) system applied surface reconstruction, the system with MD of a 5/7-atom ring core structure is stable compared with a coherently grown system at 3.0 monolayer, which is larger than that of the ideal surface. This result reveals that the energy gain due to MD formation on the reconstructed surface is smaller than that of the ideal surface. The analysis of atomic configurations of the reconstructed surface with MD confirms that the strains caused by MD formation destabilize the reconstructed surface. Furthermore, we estimate the growth behavior of InAs/GaAs(001) using macroscopic theory and find that MD formation is suppressed by surface reconstruction. These results suggest that surface reconstruction is crucial for MD formation and the resultant growth behavior.

Effect of sub-surface hydrogen on intrinsic crack tip plasticity in aluminium

ABSTRACTThe effects of sub-surface hydrogen and mixed mode loading on dislocation emission in aluminium are studied using a combination of techniques including crack simulations with an empirical interatomic potential, generalised stacking fault energy (GSF) calculations, with empirical interactions and Density Functional Theory, and the model by Rice which links the critical stress intensity factor to the unstable stacking energy. The crack orientation is and the loading is composed of a moderate traction along and a shear along , such that Shockley partials are emitted along the crack plane. The role of the relaxations around the H atoms and of the concentration of H in the glide plane, in the GSF calculation, is revealed by comparing Rice's model to the results of brute force simulations. The enhanced GSF is then calculated ab initio. The conclusion is a large decrease of the critical load to emit a dislocation, due to the displacement transverse to the glide direction. The effect of sub-surface hydrogen is negligible with respect to the mechanical one.

Theoretical investigations on the structural stability and miscibility in BAlN and BGaN alloys: bond-order interatomic potential calculations

Structural stability and miscibility of BxAl1−xN and BxGa1−xN alloys including 3-fold coordinated hexagonal (Hex), 4-fold coordinated wurtzite (WZ) and zinc blende (ZB) structures are systematically investigated based on empirical bond-order potential (BOP), which incorporates electrostatic energies due to bond and ionic charges. We find that the Hex structure is stabilized for free-standing BxAl1−xN and BxGa1−xN alloys with boron composition while the ZB (WZ) structure is stabilized for () both in BxAl1−xN and BxGa1−xN alloys due to the electrostatic energy. Furthermore, the miscibility of BxAl1−xN and BxGa1−xN alloys with 4-fold coordinated structures are found to be higher than that with Hex structure owing to the chemical energy contribution to the excess energy. These results suggest that our empirical interatomic potentials approach is feasible to clarify structural stability not only for the 3-fold coordinated Hex structure but also 4-fold coordinated WZ and ZB structures in BxAl1−xN and BxGa1−xN alloys.