Common Neighbor Analysis Research Articles

Score-based denoising for atomic structure identification

We propose an effective method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter. Our method iteratively subtracts thermal noises or perturbations in atomic positions using a denoising score function trained on synthetically noised but otherwise perfect crystal lattices. The resulting denoised structures clearly reveal underlying crystal order while retaining disorder associated with crystal defects. Purely geometric, agnostic to interatomic potentials, and trained without inputs from explicit simulations, our denoiser can be applied to simulation data generated from vastly different interatomic interactions. The denoiser is shown to improve existing classification methods, such as common neighbor analysis and polyhedral template matching, reaching perfect classification accuracy on a recent benchmark dataset of thermally perturbed structures up to the melting point. Demonstrated here in a wide variety of atomistic simulation contexts, the denoiser is general, robust, and readily extendable to delineate order from disorder in structurally and chemically complex materials.

Atomic thermal fluctuation reduction method for robust local lattice structure identification in finite-temperature molecular dynamics

Classical molecular dynamics (MD) is extensively employed to explore the properties, deformations, and fractures of materials at the atomic scale. Identifying local structures is crucial for understanding the mechanisms behind material deformation and fracture. Nevertheless, analyzing the local lattice structure at high temperatures poses challenges due to atomic thermal fluctuations, which act as noise and potentially lead to misjudgment of the local lattice structure. To date, various strategies have been implemented to circumvent this issue. However, they cannot be a solution because it is unable to reproduce phenomena unique to high temperatures, whereas others require significant computational resources. This paper introduces an innovative method to reduce atomic thermal fluctuations using a straightforward algorithm, thereby facilitating accurate identification of local lattice structures even at high temperatures. Our approach incorporates novel degrees of freedom, termed ‘Markers,’ that are linked to atoms. By reducing the thermal fluctuation of these Markers, precise analysis of the local lattice structure becomes feasible. The efficacy of this method is validated through its thermal reducibility and Markers trackabilities to atoms. Utilizing common neighbor analysis, the error rate for structure identification with our method is nearly 0% at temperatures up to 1200 K in Fe, in contrast to approximately 5% without it. Furthermore, the average distance between atoms and Markers remains below 0.1 Å. Applying our method to phase transformations, we successfully observed the transition from face-centered cubic to body-centered cubic structure in Fe at 1200 K. This method holds promise for expanding the capabilities of MD simulations at high temperatures.

Atomistic simulation of temperature dependence of tensile properties and estimation of DBTT of Cu–Ag core–shell nanowires

An atomistic model of Cu-Ag core–shell nanowire using molecular dynamics is proposed. The simulation model was utilized for the evolution of temperature dependent (in the range of 10–500 K) tensile behavior and the identification of ductile-to-brittle transition temperature (DBTT) of single-crystal Cu-Ag core–shell nanowire. The evolution of defects such as point defects, dislocations, and stacking faults, arising from tensile deformation in the mentioned temperature rage were analyzed. The Wigner-Seitz Defect analysis, Dislocation analysis, and Common Neighbor analysis methods were implemented in this study to estimate the various defects concentration in the nanowire. The study identified that Cu-Ag core–shell (core diameter = 4 nm and shell thickness = 1 nm nanowire) exhibits DBTT somewhere in the temperature between 25 K to 30 K. The modulus of elasticity found to be increasing from 19.7 TPa at 10 K to 23.7 TPa at 30 K (within brittle regime), followed by a steep fall to 14.9 TPa at 500 K where fracture seems to be in ductile mode. The observed mechanical properties of the nanowire are discussed in the article in view of the dislocation and stacking faults generation in the core–shell nanostructures.

Efficient Implementation of the Binary Common Neighbor Analysis for Platinum-Based Intermetallics

The common neighbor analysis (CNA) for binary systems is a powerful method used to identify chemical ordering in intermetallics by unique indices. The capability of binary CNA, however, is largely restricted by the availability of indices for various ordered phases. In this study, CNA indices of 11 ordered phases derived from a face-centered cubic structure were introduced on a case-by-case basis. These phases, common in intermetallics containing platinum-group metals, include C11b, MoPt2, C6, B11, AgZr, A2B2[111], A2B2[113], Pt3Tc, A3B[011], A3B[111], and A3B[113]. The chemical order in static chemical perturbation, dynamic phase competition, and experimentally reconstructed nanophase alloys were identified using binary CNA. The results indicated that the proposed version of binary CNA exhibited significantly higher accuracy and robustness compared to the short-range order, polyhedral template matching, and the original binary CNA method. Benchmarked against available methods, the formation, decomposition, and competition of specifically ordered phases in bulks and nanoalloys were well reflected by present CNA, highlighting its potential as a robust and widely adopted tool for deciphering chemical ordering at the atomic level.

Microstructural Evolution of Al under Computational Analysis of Uniaxial [100] Compression

The strain rate exerts a profound influence on the mechanical characteristics of nanomaterials. To investigate this phenomenon, the molecular dynamics approach was employed to examine the impact of uniaxial compression along the [100] crystallographic direction in monocrystalline Al. The purpose of this research was to determine the differences in reactions observed during the elastic and plastic phases. It employed the Embedded Atom Method (EAM) as well as the Modified Embedded Atom Method (MEAM) potentials at 300 K. A comparative analysis of the outcomes from these potentials demonstrated considerable disparities. The results encompassed the percentage distribution of crystal structures (fcc, hcp, bcc, and others) as well as their atomic configurations. Several analytical factors were examined, including the strain-stress curve, the radial distribution function (RDF), the common neighbor analysis (CAN). The applied MEAM potential represents a subsequent occurrence of transitions following EAM, encompassing both increasing and decreasing phase transitions.

Theoretical investigation of (La4O6)n, (La2Ce2O7)n, and (Ce4O8)n nanoclusters (n = 10, 18): Temperature effects and O-vacancy formation.

We report a theoretical investigation of temperature, size, and composition effects on the structural, energetic, and electronic properties of the (La4O6)n, (La2Ce2O7)n, and (Ce4O8)n nanoclusters (NCs) for n = 10, 18. Furthermore, we investigated the single O vacancy formation energy as a function of the geometric location within the NC. Our calculations are based on the combination of force-field molecular dynamics (MD) simulations and density functional theory calculations. We identified a phase transition from disordered to ordered structures for all NCs via MD simulations and structural analysis, e.g., radius changes, radial distribution function, common neighbor analysis, etc. The transition is sharp for La36Ce36O126, La20Ce20O70, and Ce72O144 due to the crystalline domains in the core and less abrupt for Ce40O80, La40O60, and La72O108. As expected, radius changes are abrupt at the transition temperature, as are morphological differences between NCs located below and above the transition temperature. We found a strong dependence on the O vacancy formation energy (Evac) and its location within the NCs. For example, for La40O60, Evac decreases almost linearly as the distance from the geometric center increases; however, the same trend was not observed for Ce40O80, while there are large deviations from the linear trend for La20Ce20O70. Evac has smaller values for Ce40O80 and higher values for La40O60, that is, almost three times, while Evac has intermediate values for mixed oxides, as expected from weighted averages. Therefore, the mixture of one formula unit of La2O3 with two formula units of CeO2 has the effect of increasing the stability of CeO2 (binding energy), which increases the magnitude of the formation energy of the O vacancy.

FeNi steel alloys under effect of molten lead liquid at high temperature 750℃: A molecular dynamics simulation study

Materials that have superior properties are indispensable for technological applications. Steel that has strong, heat- resistant, corrosion-resistant properties is indispensable for application in the design of liquid lead metal-cooled nuclear reactors. One of the steels that need to be studied for superior properties is FeNi alloy steel. In this study, the influence of FeNi steel composition was studied using computational methods of simulating molecular dynamics. The exact composition of FeNi steel to be able to work in liquid lead reactor coolant was analyzed using the CNA (common neighbor analysis) method. Molecular dynamics simulation uses the MOLDY program while the calculation of CNA values uses the OVITO program. From the simulation results, it can be known the effectiveness of variations in Fe and Ni composition that are able to produce the most stable FeNi alloy steel from the influence of liquid lead metal corrosion.

Deformation and boundary motion analysis of a faceted twin grain boundary

In this article, molecular dynamics simulations are used to understand how a nickel bicrystal with faceted incoherent Σ3 grain boundaries responds to uniaxial tensile loading. The deformation response is studied over a wide range of temperatures (100 – 900 K) and strain rates (107 – 1010 s−1). The dislocation extraction algorithm and common neighbor analysis are employed to identify the deformation mechanisms. Our results reveal that the yield stress decreases with temperature and increases with strain rate; whereas the elastic modulus decreases with temperature and is independent of strain rate. Furthermore, incipient plasticity is detected ahead of the yield point at lower temperatures and lower strain rates. Interestingly, the incoherent twin grain boundaries are quite mobile under the uniaxial tensile loading at lower temperatures and lower strain rates. But this mobility decreased at higher temperatures and higher strain rates, thereby, confirming this faceted grain boundary's non-Arrhenius (anti-thermal) migration behavior even under mechanical loading. From a deformation perspective, the incoherent twin facet of the grain boundary served as the major source for stacking fault formation at lower temperatures and higher strain rates. However, with the increase in temperature, the stacking faults became shorter and originated from both the incoherent twin facet and the tips of coherent twin facet. These results are in qualitative agreement with the experimental results documented in the literature.

Structural classification of Ag and Cu nanocrystals with machine learning.

We use machine learning (ML) to classify the structures of mono-metallic Cu and Ag nanoparticles. Our datasets comprise a broad range of structures - both crystalline and amorphous - derived from parallel-tempering molecular dynamics simulations of nanoparticles in the 100-200 atom size range. We construct nanoparticle features using common neighbor analysis (CNA) signatures, and we utilize principal component analysis to reduce the dimensionality of the CNA feature set. To sort the nanoparticles into structural classes, we employed both K-means clustering and the Gaussian mixture model (GMM). We evaluated the performance of the clustering algorithms through the gap statistic and silhouette score, as well as by analysis of the CNA signatures. For Ag, we found five structural classes, with 14 detailed sub-classes, while for Cu, we found two broad classes (crystalline and amorphous), with the same five classes as for Ag, and 15 detailed sub-classes. Our results demonstrate that these ML methods are effective in identifying and categorizing nanoparticle structures to different levels of complexity, enabling us to classify nanoparticles into distinct and physically relevant structural classes with high accuracy. This capability is important for understanding nanoparticle properties and potential applications.

Theoretical study of the structural and energetic properties of Ce1-xZrxO2 nanoparticles via molecular dynamics simulations.

The combination of ceria (CeO2) with different metal oxides (MO2), e.g. Ce1-xMxO2, has been strategically used to enhance its intrinsic properties. Moreover, the controlled synthesis of mixed oxide nanoparticles (NPs) opens the opportunity to explore the size dependence and chemical composition of the physical-chemical properties. However, our atomic-level understanding of how the physical-chemical and thermodynamic characteristics change with particle size and composition remains far from satisfactory. Here, we used force-field molecular dynamics simulations to investigate the effects of composition (x) and size on the physical-chemical properties of Ce1-xZrxO2 NPs with diameter from 1 (32 cations) up to 3 nm (256 cations), where x = 0.0, 0.2, 0.4, 0.6, 0.8 and 1.0. We found abrupt changes in potential energy versus temperature for NPs with more than 108 cations, indicating a structural phase transition from disordered to ordered structures, which was confirmed by the radial distribution function. We found a linear relationship between the phase transition temperature (Tpt) and the size and composition of the NPs: the increase in the molar fraction of Zr4+ and the reduction in particle size are related to lower Tpt temperature and less defined decays of potential energy versus temperature. NPs larger than 56 cations show a radial distribution function with several peaks, which is related to the order of cations and anions in these structures. These peaks gradually disappear as the size decreases and the fraction of Zr4+ increases. Similar trends were observed with X-ray diffraction analysis; for example, fluorite-like motifs occur even with 56 cations in the case of ceria, but only for NPs with 108 cations for zirconia. Common neighbor analysis confirmed that NPs with well-defined values of the temperature Tpt have face-centered cubic (fcc)-like domains in the core region. The number of ordered fcc cations increases with increasing NP size and decreasing Zr4+ concentration. Finally, we observed that ceria nucleate first during simulated annealing and occupy highly coordinated sites within the core, while Zr4+ prefers the lowest coordinated sites on the surface.

Effect of grain size and wire size on mechanical properties of polycrystalline Ta nanowire: Molecular Dynamics simulation

The effects of grain size and length-to-diameter ratio (LDR) on the mechanical properties of Polycrystalline Tantalum (Ta) nanowire were investigated by Molecular Dynamics (MD) simulation as a result of uniaxial tension deformation applied at 300 K temperature. The Embedded Atom Method (EAM) was used to determine the forces acting on the nanowire atoms. Young's modulus (E), yield strength and fracture stress values were determined from the stress-strain relationship determined as a result of the deformation process. Microstructural changes that occur as a result of plastic deformation from atomic positions determined using the common neighbor analysis method (CNA) were examined. It was determined that the grain size and LDR had a significant effect on the deformation behavior of the Ta nanowire, and the plastic deformation and fracture were caused by the rearrangement of atomic positions by the surface effect. It was found that nanowires with small grain size and LDR exhibited superplastic behavior. In the modeled polycrystalline nanowire system, it was determined that the grain size affects the movement mechanisms of the grains, grain boundaries and the relationship between grain size and yield strength. From this relationship, Hall-Petch effect and after a certain critical grain size inverse Hall-Petch effect were observed.

Examination of mechanical behaviour of fullerene doped aluminium matrix composite produced by sintering process

The primary goal of this research is to understand the mechanical properties of Aluminium composite structures doped with different fullerene types. Therefore, irregularly shaped nanoparticles were created by utilizing a new generation method and the fullerene structures randomly sprinkle between Aluminium nanoparticles. Following this process, a sintering procedure was applied by using molecular dynamic simulations in order to obtain an atomistic model of a doped Aluminium matrix composite. In addition, adaptive common neighbour analysis (aCNA) was used to track the evolution of atomistic model morphology. Furthermore, the mechanical tests were carried out by using molecular dynamic simulations in order to understand the effect of the fullerene structures on the mechanical performance. Improvement in mechanical properties was achieved by adding fullerene to the aluminium matrix. Additionally, it has been observed that fullerene diameters have a significant effect in terms of yield and maximum tensile strength. This study proposes a new composite material with a mechanical performance created by a modelling approach that best represents real production technology.

Effect of cooling rate on solidification points and atomic structures of metal nanoclusters: a molecular dynamics simulation study

In this work, the micro-structural evolution during the solidification process of three metal nanoclusters (copper, nickel, and silver) containing 2048 atoms each, subjected to different cooling rates are investigated. Molecular dynamics (MD) simulations in conjunction with modified embedded atom method (MEAM) potentials have been employed to carry out the work. For different cooling rates, the solidification points of the nanoclusters are estimated from the variation in average potential energy of the systems during the crystallization process, and the structural evolutions are analysed by utilising radial distribution function and common neighbour analysis method. The results advocate that the cooling rate has a profound impact on the structures of the solidified nanoclusters. The critical cooling rate for each nanocluster has been captured, where a glassy/amorphous structure is observed. Polycrystalline nano-structures (consisting of amorphous, FCC and HCP) are observed below these critical cooling rates and the number of close-packed structures (FCC, HCP) are found to decrease with the increase in cooling rate. The findings of this work would be helpful in various practical applications of the metal nanoclusters, where the structure of nanoclusters directly affect.

Effect of temperature, pressure, crystal defect types, and densities on the mechanical behavior of tungsten under tensile deformation: A molecular dynamics simulation study

The mechanical behavior of tungsten under tensile deformation was investigated using molecular dynamics simulations, with a focus on the effects of temperature, pressure, crystal defect types, and densities. Single vacancies, spherical voids, and self-interstitials were studied at concentrations ranging from 0.1% to 2%. The Common neighbor analysis reveals that the crystal structure underwent a phase transition from the body-centered cubic to the face-centered cubic structure at increasing levels of applied strain, followed by the formation of a disordered phase. Increasing temperature reduced the mechanical properties of tungsten, including yield strength, yield strain, modulus of elasticity, and ultimate tensile stress, possibly due to the activation of dislocation nucleation and propagation, resulting in the formation of dislocation loops. Increasing pressure enhanced the mechanical properties of tungsten, with the formation of dislocation loops occurring at much lower strains at higher pressures. All mechanical parameters decreased with increasing defect density, with self-interstitial defects having a more severe effect than single vacancies and spherical voids. The number and length of dislocation loops also increased with increasing defect density. Temperature and pressure had varying effects on the stress–strain curve of tungsten with different defect types and densities, with increasing pressure generally enhancing its mechanical behavior and increasing temperature decreasing it. These findings provide valuable insights into the design and development of tungsten-based materials with enhanced mechanical properties.

Exploring the structural and mechanical properties of single-Component Mo metallic glasses

In this paper, we employed molecular dynamics (MD) simulations to investigate how the cooling rate affects the structural and mechanical properties of molybdenum monoatomic glasses (Mo-MG). We also examined the influence of cooling rates on the glass transition temperature, showing a decrease as the cooling rates decreased. The analysis of structural properties using techniques such as radial distribution function (RDF), Voronoi tessellation analysis, and common neighbor analysis (CNA) revealed that the cooling rates have a significant impact on the local structure of Mo-MG. The RDF function displayed a splitting in the second peak, indicating the formation of a super-cooled liquid state. The CNA analysis identified the most dominant local structures as icosahedral configurations, specifically ⟨0,1,10,2⟩, ⟨0,0,12,0⟩, and ⟨0,0,12,2⟩. The mechanical properties were calculated using the Virial theorem implemented in the LAMMPS simulation software. The results indicated that the ultimate strengths of Mo-MG were comparable to those of crystalline molybdenum. However, the ultimate strength decreased with increasing cooling rate, indicating that higher cooling rates result in weaker Mo-MG samples. A similar trend was observed for Young's modulus, where increasing cooling rates led to a decrease in the stiffness of the formed Mo-MG.

Effect of pores on microscopic wear properties and deformation behavior of Ni-Cr alloy coating.

Molecular dynamics (MD) was carried out to simulate the friction behavior of Ni-Cr alloy coating containing pores. The mechanical properties, displacement, abrasion depth, and defect change patterns of the coating under nano-friction were studied. It was found that the stacking fault would extend to the pores, and both tangential and normal forces decreased when the grinding ball was above the pores. Meanwhile, the pores changed the extension direction of shear strain inside the coating, and stress concentrations were generated at the pores. In addition, the deformation behavior inside the coating was influenced by the processing depth, the smaller the relative height of the grinding ball and the pore, the greater the atomic deformation around the pore. The pores changed the path of atomic movement, resulting in less deformation of the coating below the pores. The presence of pores promoted the generation of surface steps and increased the amount of wear on the coating. It was also found that pores facilitated energy release and provided space for dislocation extension, and the large accumulation of dislocations led to frictional strengthening near the pores, which enhanced the properties of the material below the pores. It was found that the increase of the pore size caused the normal force decrease and the wear performance of the coating decrease, but the thermal insulation performance would be improved. In this paper, nanoscale modeling was performed in the large-scale atomic/molecular parallel simulator (LAMMPS) simulation environment. The model was visualized and analyzed in three dimensions by Open Visualization Tool (OVITO), the common neighbor analysis (CNA) method was used to obtain the atomic structure information, and the dislocation analysis (DXA) method was applied to obtain the dislocations.

Robust crystal structure identification at extreme conditions using a density-independent spectral descriptor and supervised learning

The increased time- and length-scale of classical molecular dynamics simulations have led to raw data flows surpassing storage capacities, necessitating on-the-fly integration of structural analysis algorithms. As a result, algorithms must be computationally efficient, accurate, and stable at finite temperature to reliably extract the relevant features of the data at simulation time. In this work, we leverage spectral descriptors to encode local atomic environments and build crystal structure classification models. In addition to the classical way spectral descriptors are computed, i.e. over a fixed radius neighborhood sphere around a central atom, we propose an extension to make them independent from the material’s density. Models are trained on defect-free crystal structures with moderate thermal noise and elastic deformation, using the linear discriminant analysis (LDA) method for dimensionality reduction and logistic regression (LR) for subsequent classification. The proposed classification model is intentionally designed to be simple, incorporating only a limited number of parameters. This deliberate simplicity enables the model to be trained effectively even when working with small databases. Despite the limited training data, the model still demonstrates inherent transferability, making it applicable to a broader range of scenarios and datasets. The accuracy of our models in extreme conditions (high temperature, high density, large deformation) is compared to traditional algorithms from the literature, namely adaptive common neighbor analysis (a-CNA), polyhedral template matching (PTM) and diamond structure identification (IDS). Finally, we showcase two applications of our method: tracking a solid–solid BCC-to-HCP phase transformation in Zirconium at high pressure up to high temperature, and visualizing stress-induced dislocation loop expansion in single crystal FCC Aluminum containing a Frank–Read source, at high temperature.

Bramble: adaptive common neighbor analysis (CNA) for the recognition of surface topologies in nanoparticles

Bramble: adaptive common neighbor analysis (CNA) for the recognition of surface topologies in nanoparticles

Structural and Small-Angle Scattering Analysis on Melting of Gold Nanoparticle

Molecular Dynamics (MD) simulation was used for time evolution of melting dynamics on gold nanoparticle with thickness 8 nm. The systems are heated up from room temperature up to three times melting point in 10 ps to ensure that the system is melted and expanding. After t = 7 ps, the system is collapsed, hence pressure oscillation is vanished. Common Neighbor Analysis (CNA) along with wide angle scattering confirmed the melting state at the end of simulation. While small-angle scattering indicated the expansion of the system, it cannot precisely calculate system radius due to its small size.

Concentration dependence of microstructural evolution of Zr-Pt liquid alloys: A molecular dynamics study

The concentration dependence of the atomic structure and glass formation process of Zr100-xPtx (10 ≤ x ≤ 60) alloys were investigated by molecular dynamics simulations in this study. The agreement between the structural functions and the experimental data showed that the local order is reliably predicted. It was observed that the Zr40Pt60 alloy has the highest glass transition temperature and the fraction of crystal-like clusters. Common neighbor analysis revealed that the icosahedra and icosahedra-like clusters are more frequent for x < 50 concentrations, while bcc-like clusters are effective for x ≥ 50. It was seen that the local environment around Pt atoms would be more flexible, while Zr atoms would more easily adopt a preferential local packing according to their preferences. Present findings suggest that the strong chemical affinity between Zr and Pt plays an influential role in the nucleation of local structure in the system, and the addition of Pt weakens the icosahedra short-range order.