Large-scale Molecular Dynamics Research Articles

Phase transition in medium entropy alloy CoCrNi under quasi-isentropic compression

Under high strain-rate loading, prominent increases in pressure usually triggers phase transition (PT), but the concomitant temperature rise may also cause melting. Quasi-isentropic (QI) compression provides a strategy to explore solid-state phase transition by reducing the temperature rise while retaining high pressure. Using large-scale molecular dynamics simulations, we investigate PTs in single crystal CoCrNi medium entropy alloys (MEAs) under QI compression. With the applied strain rates ranging from 108 s−1 to 1011 s−1, the strain-rate dependence and anisotropy of yield stress and solid-state PT path are revealed by comparing the mechanical responses along three compressed crystallographic orientations ([100], [11¯0], and [111]). Positive strain-rate sensitiveness is found in the yield stress along the [11¯0] and [111] directions, while insensitiveness along the [100] direction. Various PTs occur alongside massive plastic deformation in the post-yield regime. As the strain rate rises, face-centered-cubic (FCC) to body-centered-cubic (BCC) PT overrides the stacking fault-induced hexagonal-close-packed (HCP) phase formation and dominates the plasticity for the [100] loading. By contrast, crystalline PTs give way to amorphization for [11¯0] and [111] loading at high strain rates. Chemical short-range order hinders dislocation slip and promotes dislocation interactions, which further facilitate early formation of the BCC phase, suggesting a potential strategy to tailor polymorphism in MEAs.

Insight into photo-Fenton reaction mechanism on a magnetite-GO nanocomposite: Computational and experimental investigations

A necessary condition for an optimum photo-Fenton heterojunction photocatalyst is that its reduction part should be electron-rich and have a high affinity for H2O2 molecules. This study uses a combination of experimental investigations, classical molecular dynamics (MD), and density functional theory (DFT) calculations to elucidate the photo-Fenton degradation of p-nitrophenol (PNP) on a magnetite-GO composite from the above viewpoint. The magnetite nanostructures and the Graphene oxide (GO) for this composite had bandgaps in the visible range. Photo-Fenton experiments revealed that the composite had significantly better PNP degradation activity than magnetite nanoparticles. Large-scale molecular dynamics demarcated the affinity of the different parts of the composite towards H2O2 and PNP in an aqueous medium. Time-dependent DFT (TDDFT) calculations revealed that photo-excitation shifted the highest occupied molecular orbitals (HOMO) to the magnetite part of the composite. The photo-Fenton mechanism, proposed using computational and experimental pieces of evidence, gives a critical criterion for screening photocatalysts for a particular substrate.

Does speed kill or make friction better?—Designing materials for high velocity sliding

Many modern applications rely on the safe operation of components sliding at high speeds, e.g., in the fields of e-mobility, high-speed manufacturing, or impact-resistant materials. While the impact of mechanical energy and heat on the friction and wear behavior of dry metallic interfaces has been the focus of extensive research in the past, the effect of extreme speeds on the near-surface deformation mechanisms in polycrystalline metals has remained poorly understood. In this work, we have tackled this crucial issue by exploring sliding velocities ranging from 10 to 2560 m/s via large-scale molecular dynamics simulations to identify three distinct deformation regimes in CuNi alloys. The microstructural response does not vary much for any of the considered systems up to sliding velocities of 40 m/s, but then evolves drastically up to a maximum value located between 320 and 1280 m/s depending on the composition. We observe that the degree of plastic deformation at the highest sliding speeds drops down to a level almost as low as for the lowest sliding speeds. We attribute this behavior to a sharp increase in the contact temperature at these high sliding speeds, approaching or even exceeding the bulk melting temperature, which goes hand in hand with a decline in the contact area and the resistance to sliding. Our characterization of the non-linear and non-monotonic material response to increasing sliding velocities will aid the systematic selection of materials and the design of robust components for a variety of high-speed sliding and wear applications.

Extemporaneous Mechanochemistry: Shock-Wave-Induced Ultrafast Chemical Reactions Due to Intramolecular Strain Energy

Regions of energy localization referred to as hotspots are known to govern shock initiation and the run-to-detonation in energetic materials. Mounting computational evidence points to accelerated chemistry in hotspots from large intramolecular strains induced via the interactions between the shock wave and microstructure. However, definite evidence mapping intramolecular strain to accelerated or altered chemical reactions has so far been elusive. From a large-scale reactive molecular dynamics simulation of the energetic material 1,3,5-triamino-2,4,6-trinitrobenzene, we map decomposition kinetics to molecular temperature and intramolecular strain energy prior to reaction. Both temperature and intramolecular strain are shown to accelerate chemical kinetics. A detailed analysis of the atomistic trajectory shows that intramolecular strain can induce a mechanochemical alteration of decomposition mechanisms. The results in this paper could inform continuum-level chemistry models to account for a wide range of mechanochemical effects.

Chemical structure effects on coal pyrolyzates and reactions by using large-scale reactive molecular dynamics

The relationship between the coal chemical structure and its thermal reactivity is vital to understand coal pyrolysis behaviors. In order to explore chemical structure effects on pyrolysis process, five large-scale coal models of different ranks were constructed and simulated with ReaxFF MD simulations by a combined approach of high performance computing and cheminformatics based reaction analysis in this work. The qualitative temperature mapping results between ReaxFF MD simulations and thermogravimetry experiments were obtained for the first time through the detected covalent bond breaking, which suggests a promising scheme to map the simulation results to the real world. Importantly, the typical structures in coal can be used as the indicators to predict pyrolysis stages, weight loss profiles and major pyrolyzate distributions from the atomistic level. The starting temperature of coal thermal decomposition is anchored by the parameters of falO in 13C NMR representing alkyl ether amounts; and the largest phenol tar generation links closely to the faP NMR parameter; meanwhile methoxy groups determine the initial generation of CH3 radicals and CH4 at relatively low temperature. Additionally, the dynamic profiles of Car-Car bonds and the second increasing trend for CH3 radicals have strong relationship with recombination reactions. With the reasonable coal structures, the large-scale ReaxFF MD simulation alone can complement experimental observation comprehensively to understand the complex coal thermal chemistry and used as a preliminary screening approach to select the coal type or rank for industrial utilization.

Is icosahedral short-range order presented in supercooled transition metals?

Supercooled transition metals are characterized by the absence of long-range order and the presence of a specific short-range order in the arrangement of atoms. So, the presence of shoulders and broadenings at the second maximum in the experimentally measured quantity—in the static structure factor S(k)—is usually interpreted as a manifestation of the icosahedral (ideal or distorted) short-range order (ISRO—Icosahedral Short-Range Order). Icosahedral short-range order is a structure with fivefold symmetry in the arrangement of atoms, which can lead to the possibility of achieving deep supercooling. In this work, we study the local structural features of equilibrium and supercooled nickel melts under various cooling protocols ( K s−1) in order to clarify the mechanism of formation of the icosahedral short-range order in pure transition metals. Comprehensive studies of the properties of nickel melts were carried out using experiments on x-ray diffraction, large-scale molecular dynamics (MD) simulations with subsequent structural and cluster analysis. A good agreement was found between the results of x-ray diffraction data and the MD simulations results for an equilibrium nickel melt. It was found that the nickel melt is characterized by a short-range order, formed by fragments of icosahedra and distorted icosahedral clusters. It was revealed that the ‘liquid-crystal’ phase transition in nickel is accompanied by the transformation of distorted icosahedral clusters into clusters with the fcc/hcp-symmetry. It is shown that, in contrast to the Voronoi tessellation method, the cluster analysis method based on the () rotational invariants does not allow sufficiently correct identification of the distorted icosahedral short-range order in metal melts.

(Digital Presentation) Pinpointing the Potential-of-Zero-Charge in a Alumina-Coated Aluminum/Water Interface Model for Corrosion Applications

The surfaces of most metals immersed in electrolytes feature a several nanometer-thick oxide/hydroxide layer in aqueous electrolytes. This leads to the existence of both a metal/oxide and an oxide/liquid electrotlyte interface. The resulting complexity, and the uncertainty about the structure of and the charges on the surface films, make it challenging to predict from first principles the potential-of-zero-charge (PZC) which has been correlated to the pitting potential [1]. In this work, we use large-scale Density Functional Theory and ab initio molecular dynamics to calculate the PZC of a Al(111)|gamma-Al(2)O(3)|water model within the context of aluminum corrosion. By partitioning the multiple, complex interfaces involved into binary components with additive contributions to the overall work function and voltage, we calculate the PZC in liquid water for this model. Simultaneously, we calculate the orbital energy levels of defects like oxygen vacancies in gamma-Al(2)O(3), which are critical parameters in theories associated with pitting corrosion onset. Our predictions align the Fermi level at PZC with oxygen vacancy impurity defect levels, and estimate the voltage needed to generate charged oxygen vacancies in the flat band approximation. The similarities and differences in results and assumptions used between our model calculations and experiments will be discussed.[1] J.~O'M. Bockris and Y.~Kang, J. Solid Electrochem. 1:17 (1997).Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the document do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Figure 1

Richtmyer--Meshkov instability with ionization at extreme impact conditions

Richtmyer–Meshkov instability (RMI) under extreme impacting conditions is studied via molecular dynamics (MD) simulation with an electron force field (eFF) model. It is revealed that the strong loading ionizes materials into heavy ions and free electrons, and subsequently, a quasi-steady electron/ion separation zone is established across the shock front because free electrons can move quickly to regions ahead of the shock wave. The electron/ion separation zone propagates at the same velocity as that of the shock wave, and its width and strength remain nearly constant. Based on this observation, a simple charge distribution profile is proposed for microscopic RMI with ionization, with which an analytical model for interface acceleration caused by electric field force can be derived. A nondimensional parameter (η), which is defined as the ratio of the flow domain length to the length of the charge separation zone, is proposed. When η exceeds a certain value, the charge density distribution is similar to that of macroscopic RMI with ionization, and thus, an acceleration model for macroscopic RMI can be derived. Finally, a nonlinear model for the perturbation growth of macroscopic RMI with ionization is achieved by incorporating the acceleration model to the potential flow theory of Q. Zhang and W. Guo [“Universality of finger growth in two-dimensional Rayleigh–Taylor and Richtmyer–Meshkov instabilities with all density ratios,” J. Fluid Mech. 786, 47–61 (2016)]. The validity of the model is verified by the present large-scale eFF MD simulation and experimental results obtained with the Nova laser.

Molecular Dynamics Investigation of Clustering in Aqueous Glycine Solutions.

Recent experiments with undersaturated aqueous glycine solutions have repeatedly exhibited the presence of giant liquid-like clusters or nanodroplets around 100 nm in diameter. These nanodroplets re-appear even after careful efforts for their removal and purification of the glycine solution. The composition of these clusters is not clear, although it has been suggested that they are mainly composed of glycine, a small and very soluble amino acid. To gain insights into this phenomenon, we study the aggregation of glycine in aqueous solutions at concentrations below the experimental solubility limit using large-scale molecular dynamics simulations under ambient conditions. Three protonation states of glycine (zwitterion = GLZ, anion = GLA, and cation = GLC) are simulated using molecular force fields based on the 1.14*CM1A partial charge scheme, which incorporates the OPLS all-atom force field and TIP3P water. When initiated from dispersed states, we find that giant clusters do not form in our simulations unless salt impurities are present. Moreover, if simulations are initiated from giant cluster states, we find that they tend to dissolve in the absence of salt impurities. Therefore, the simulation results provide little support for the possibility that the giant clusters seen in experiments are composed purely of glycine (and water). Considering that strenuous efforts are made in experiments to remove impurities such as salt, we propose that the giant clusters observed might instead result from the aggregation of reaction products of aqueous glycine, such as diketopiperazine or other oligoglycines which may be difficult to separate from glycine using conventional methods, or their co-aggregation with glycine.

Understanding Metal–Organic Framework Nucleation from a Solution with Evolving Graphs

A mechanistic understanding of metal-organic framework (MOF) synthesis and scale-up remains underexplored due to the complex nature of the interactions of their building blocks. In this work, we investigate the collective assembly of building units at the early stages of MOF nucleation, using MIL-101(Cr) as a prototypical example. Using large-scale molecular dynamics simulations, we observe that the choice of solvent (water and N,N-dimethylformamide), the introduction of ions (Na+ and F-) and the relative populations of MIL-101(Cr) half-secondary building unit (half-SBU) isomers have a strong influence on the cluster formation process. Additionally, the shape, size, nucleation and growth rates, crystallinity, and short and long-range order largely vary depending on the synthesis conditions. We evaluate these properties as they naturally emerge when interpreting the self-assembly of MOF nuclei as the time evolution of an undirected graph. Solution-induced conformational complexity and ionic concentration have a dramatic effect on the morphology of clusters emerging during assembly. While pure solvents lead to the rapid formation of a small number of large clusters, the presence of ions in aqueous solutions results in smaller clusters and slower nucleation. This diversity is captured by the key features of the graph representation. Principle component analysis on graph properties reveals that only a small number of molecular descriptors is needed to deconvolute MOF self-assembly. Descriptors such as the average coordination number between half-SBUs and fractal dimension are of particulalr interest as they can be can be followed experimentally by techniques like by time-resolved spectroscopy. Ultimately, graph theory emerges as an approach that can be used to understand complex processes revealing molecular descriptors accessible by both simulation and experiment.

Developing a force field for the Ba1−xCaxZrO3 ferroelectric alloy: Prediction of a ferroelectric superlattice structure

In this study, we develop a classical interatomic potential for the ${\mathrm{Ba}}_{1\ensuremath{-}x}{\mathrm{Ca}}_{x}{\mathrm{ZrO}}_{3}$ alloy based on the bond-valence theory. The bond-valence model enables rapid and large-scale molecular dynamics simulations of alloys with variable compositions. Molecular dynamics simulations based on this force field can reproduce the experimentally observed composition-dependent dielectric responses and lattice constants very well, indicating the validity and robustness of this force field. Based on molecular dynamics simulations, we demonstrate that ${\mathrm{Ba}}_{1\ensuremath{-}x}{\mathrm{Ca}}_{x}{\mathrm{ZrO}}_{3}$ can adopt a ferroelectric phase under a tensile strain, while the ${\mathrm{Ba}}_{1\ensuremath{-}x}{\mathrm{Ca}}_{x}{\mathrm{ZrO}}_{3}$ solid solution under zero strain is paraelectric. In addition, we find that strain can invoke a phase transition from antiferroelectric to ferroelectric in the ${\mathrm{BaZrO}}_{3}/{\mathrm{CaZrO}}_{3}$ superlattice. These results successfully explain previous experimental results and first-principles calculations. This interatomic potential provides a powerful tool for simulating the physical properties of perovskite alloys at nanoscale and thus has great significance in understanding the underlying physical mechanisms and designing functional materials.

Abstract 2810: Modeling pHLA:TCR interactions for effective TCR therapies: Leveraging AI and molecular dynamics

Abstract Background: The purpose of our study is to aid the development of adoptive T-cell receptor (TCR) -based cancer therapies with an AI platform that models binding between peptide-human leukocyte antigen (pHLA) complexes and TCRs. One of the main challenges in the field is finding the right pHLA target and the TCR that binds it. The development of in silico methods that can reduce the cost and time of laboratory research is the ‘holy grail’ of novel TCR therapies design. Molecular Dynamics (MD) simulation is a computational method allowing to gain a direct insight into the movements of protein complexes and giving the possibility to study the dynamics of the pHLA:TCR interaction. The combination of the MD and AI models helps us to increase the accuracy of the pHLA:TCR binding prediction. Methods: We conducted large-scale MD simulations of the crystal structures of 130 pHLA:TCR complexes available in the Protein Data Bank (PDB). Next, we analyzed the obtained trajectories in terms of formed bonds and other physico-chemical properties. This led us to postulate an MD model of interaction further used as an inductive bias for the AI binding prediction model, in which we applied deep learning on public single-cell pHLA:TCR datasets to estimate the probability of binding.In order to validate and improve our platform, we are building a pHLA:TCR interaction database with paired alpha and beta chain TCR sequences from a cohort of 100 colorectal cancer patients (NCT04994093). Results: We established the agretopicity and epitopicity of peptide residues within the pHLA:TCR systems based on the characteristics of the bonds formed between peptides and HLAs or TCRs in MD simulations. Additionally, the incorporation of other molecular properties, e.g. solvent accessible surface area, improved our AI pHLA:TCR model. In order to refine our results, we also account for the HLA type and physico-chemical properties of peptides. The introduction of the detailed TCR-peptide interaction matrix allowed the model to generalize to previously unseen pHLA targets. Conclusions: Rational design of effective TCR therapies can benefit from novel pHLA:TCR models for which the predictive accuracy is driven by the synergistic AI-MD approach. Citation Format: Alexander Myronov, Sławomir Stachura, Anna Sanecka-Duin, Oskar Gniewek, Łukasz Grochowalski, Maciej Jasiński, Paulina Król, Joanna Marczyńska-Grzelak, Piotr Skoczylas, Daniel Wojciechowski, Giovanni Mazzocco, Mikołaj Mizera, Jan Kaczmarczyk, Agnieszka Blum. Modeling pHLA:TCR interactions for effective TCR therapies: Leveraging AI and molecular dynamics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2810.

Machine learning molecular dynamics simulations toward exploration of high-temperature properties of nuclear fuel materials: case study of thorium dioxide

Predicting materials properties of nuclear fuel compounds is a challenging task in materials science. Their thermodynamical behaviors around and above the operational temperature are essential for the design of nuclear reactors. However, they are not easy to measure, because the target temperature range is too high to perform various standard experiments safely and accurately. Moreover, theoretical methods such as first-principles calculations also suffer from the computational limitations in calculating thermodynamical properties due to their high calculation-costs and complicated electronic structures stemming from f-orbital occupations of valence electrons in actinide elements. Here, we demonstrate, for the first time, machine-learning molecular-dynamics to theoretically explore high-temperature thermodynamical properties of a nuclear fuel material, thorium dioxide. The target compound satisfies first-principles calculation accuracy because f-electron occupation coincidentally diminishes and the scheme meets sampling sufficiency because it works at the computational cost of classical molecular-dynamics levels. We prepare a set of training data using first-principles molecular dynamics with small number of atoms, which cannot directly evaluate thermodynamical properties but captures essential atomistic dynamics at the high temperature range. Then, we construct a machine-learning molecular-dynamics potential and carry out large-scale molecular-dynamics calculations. Consequently, we successfully access two kinds of thermodynamic phase transitions, namely the melting and the anomalous lambda transition induced by large diffusions of oxygen atoms. Furthermore, we quantitatively reproduce various experimental data in the best agreement manner by selecting a density functional scheme known as SCAN. Our results suggest that the present scale-up simulation-scheme using machine-learning techniques opens up a new pathway on theoretical studies of not only nuclear fuel compounds, but also a variety of similar materials that contain both heavy and light elements, like thorium dioxide.

Comment on ‘Finite size effect on the existence of the liquid–vapour spinodal curve’

In a recent paper, Díaz-Herrera et al. [Mol. Phys. 122, e1989071 (2022)] have studied the condensation transition of spherical droplets in finite closed systems using large-scale Molecular Dynamics simulations. The authors find that the loci of discontinuities in the observed pressure–density isotherms do not converge to the bulk orthobaric curve, apparently at odds with results obtained previously using Monte Carlo simulations. The purpose of this comment is to point out that the Monte Carlo and Molecular Dynamics simulation results are actually mutually consistent, and the apparent differences are not related to the different sizes studied, but to the way each method implements sampling of configuration space.

Computational Characterization of N-acetylaspartylglutamate Synthetase: From the Protein Primary Sequence to Plausible Catalytic Mechanism

The methods of supercomputer molecular modeling are applied to characterize structure and dynamics of one of the key human brain enzymes, N-acetylaspartylglutamate synthetase. The three-dimensional all-atom models of the enzyme with the reactants in the active site are constructed in several steps, starting from pilot protein structure in the apo-form obtained with the AlphaFold2 from the protein primary sequence. Deposition of reactant molecules into the protein cavity, construction of the reaction intermediate and relaxation of the complex are carried out with the help of large-scale classical molecular dynamics calculations. On the top of the construct, molecular dynamics simulations with the quantum mechanics/molecular mechanics interaction potentials are performed for the most promising conformations of the model system. Analysis of the latter allows us to propose plausible catalytic mechanisms of chemical reactions in the enzyme active site. The applied computational strategy opens the way towards ab initio enzymology using modern supercomputer simulations.

Coherent and incoherent phonon thermal transport in group-III nitride monolayer superlattices with Tersoff type interatomic potential

The existence of a crossover from coherent to incoherent phonon transport for different monolayer superlattices such as h-BN/h-AlN, h-BN/h-GaN, h-AlN/h-GaN, and h-BN/h-AlN/h-GaN using large-scale non-equilibrium molecular dynamics simulations is unveiled. In this context, the lattice thermal conductivities of considered superlattice structures are obtained for varying period lengths, sample lengths, and temperatures. It is predicted that the thermal conductivities of superlattice structures can be lowered even up to 99% than that of their pristine counterparts. Also, it is revealed that a minimum thermal conductivity resulting from the competition between coherent, wave-like, and incoherent, particle-like, phonon transport can be achieved by altering the period length for all the superlattice structures. Moreover, it is observed that the thermal conductivity minimum at higher temperatures compared to low temperatures occurs at shorter period lengths for binary superlattices, and the minimum depths decrease with increasing temperature. Results point out that group-III nitride superlattices can be considered as an alternative option in controlling the heat flow for thermal management and thermoelectric device applications.

Conformational Energy Benchmark for Longer n-Alkane Chains.

We present the first benchmark set focusing on the conformational energies of highly flexible, long n-alkane chains, termed ACONFL. Unbranched alkanes are ubiquitous building blocks in nature, so the goal is to be able to calculate their properties most accurately to improve the modeling of, e.g., complex (biological) systems. Very accurate DLPNO-CCSD(T1)/CBS reference values are provided, which allow for a statistical meaningful evaluation of even the best available density functional methods. The performance of established and modern (dispersion corrected) density functionals is comprehensively assessed. The recently introduced r2SCAN-V functional shows excellent performance, similar to efficient composite DFT methods like B97-3c and r2SCAN-3c, which provide an even better cost-accuracy ratio, while almost reaching the accuracy of much more computationally demanding hybrid or double hybrid functionals with large QZ AO basis sets. In addition, we investigated the performance of common wave function methods, where MP2/CBS surprisingly performs worse compared to the simple D4 dispersion corrected Hartree-Fock. Furthermore, we investigate the performance of several semiempirical and force field methods, which are commonly used for the generation of conformational ensembles in multilevel workflows or in large scale molecular dynamics studies. Outstanding performance is obtained by the recently introduced general force field, GFN-FF, while other commonly applied methods like the universal force field yield large errors. We recommend the ACONFL as a helpful benchmark set for parametrization of new semiempirical or force field methods and machine learning potentials as well as a meaningful validation set for newly developed DFT or dispersion methods.

Experimental and molecular dynamics investigations on Z-scheme visible light Ag3PO4/CuWO4 photocatalysts for antibiotic degradation

We have developed all-solid-state Z-scheme Ag3PO4/CuWO4 two-component photocatalysts for ciprofloxacin (CIP) and tetracycline degradation under visible light irradiation. Besides the valence/conduction band positions, the adsorption behaviors of the components towards targeted organic moieties were the other factors contributing to this photocatalyst design. Aqueous medium large-scale molecular dynamics (MD) simulations delineated affinities of various species in the reaction medium towards different catalyst surfaces. The photocatalytic activity of the composite changed with the Ag3PO4 amount deposited on CuWO4 nanostructures. XPS analysis showed electron migration from the CuWO4 side to the Ag3PO4 part. Active species analysis by appropriate scavenger molecules revealed the oxidation of antibiotics on the photoexcited holes. A combination of experimental and MD observations helped formulate the probable photocatalytic mechanism.

The combined effect of carbon and chromium enrichment on 〈1 0 0〉 loop absorption in iron

In this work we study the effects of C and Cr enrichment of 〈100〉 dislocation loops (DL) on their absorption and obstacle strength when interacting with an edge dislocation. To do so, we have i) developed a C-Cr cross potential based on density functional theory data as part of a ternary FeCrC interatomic potential; ii) performed exchange Monte Carlo simulations employing the developed interatomic potential to obtain the distribution of the solutes enriching the DL in the energetically optimum configurations; iii) performed large scale molecular dynamics simulations employing the interatomic potential to characterize the interaction between an edge dislocation line and the decorated DL. We found that the obstacle stress scales to the same obstacle strength regardless the DL density. On the other hand, we found that C, the level of Cr enrichment, loop size and interaction temperature have a significant impact on the obstacle strength and level of absorption of the loops. The presented results can be used to help parameterize and validate discrete dislocation dynamics codes and therein integrated constitutive laws to enable accounting for irradiation-induced chemical segregation effects.

Dislocation-dominated void nucleation in shock-spalled single crystal copper: Mechanism and anisotropy

Large-scale molecular dynamics simulations are conducted on single crystal copper along eight representative orientations to investigate dislocation-dominated void nucleation during shock loading and spall failure, including mechanisms and anisotropy. Spall strength estimated from free surface velocity decreases in the order of group I ([001]), group IV ([012] and [011]), group III ([122] and [123]) and group II ([114], [112] and [111]), respectively, in good agreement with anisotropy of spall strength in previous experiments and simulations. A lower spall strength is statistically associated with a higher void nucleation rate and a higher density of stable immobile dislocations (1/6〈110〉 and 1/3〈100〉) formed before void nucleation. Stable immobile dislocation plays a key role in void nucleation. The formation of stable immobile dislocations requires three conditions: high resolved shear stress for activating mobile dislocations, two or more main slip planes for dislocation reaction, and high angle between activated slip directions for high stability of immobile dislocations. Based on crystal elastic–plastic theory, resolved shear stress on all slip systems is calculated to analyze orientation effects on the three conditions. The three conditions can be fulfilled by group II orientations, but cannot by group I, III, and IV orientations, leading to anisotropy in void nucleation and spall strength.