Reactor pressure vessels (RPVs) used in Generation II light water reactors for commercial nuclear power are prone to neutron-irradiation hardening during operation. To certify safety and perform life-extension analysis, hardening models that consider irradiation-induced precipitation are used, based on phenomenological or empirical evidence. Dislocations play a pivotal role in hardening, and thus, understanding dislocation-precipitate interactions aids the improvement of these models. The objective of this work is to compute the critical shear stress necessary for bypass of an edge dislocation in Fe through a field of Mn-Ni rich precipitates, which form in large number densities at high neutron fluence in RPVs. Using the discrete dislocation dynamics method, simulations show that the critical shear stress for bypass increases with precipitate density and diameter. A similar conclusion is made when precipitate misfit, or eigenstrain, is included. A previously proposed modification to the Bacon-Kocks-Scattergood precipitate hardening model is found to better describe dislocation behavior through sparse precipitate regions. Low precipitate densities below a threshold value show greater variation in the critical shear stress for precipitate field bypass.

Nonequilibrium molecular dynamics simulations are employed to characterize elastodynamic wave emission from dislocation nucleation events in shock compressed Al along the (110) and (111) orientations. Oscillations in pressure (or shear stress) in the elastically compressed region during shock wave propagation are identified and traced backwards in time to the elastic-plastic interface, showing their alignment with discrete jumps in plastic wave velocity. It is found that the details associated with dislocation nucleation events change considerably as shock pressure increases. At low shock pressures, but above the Hugoniot elastic limit, dislocations are nucleated heterogeneously and continuously at the elastic-plastic interface with periodic homogenous nucleation events a short distance in front of the elastic-plastic interface. At higher shock pressures, in addition to heterogeneous events, discrete bursts of dislocations can nucleate several nanometers in front of the elastic-plastic interface. These details and the transition between regimes are captured in this work only because of the length of the simulation models used, which are well beyond what is typically employed in molecular dynamics simulations. Understanding the mechanisms associated with dislocation nucleation during shock, and their length and time scales, is critical to inform higher length scale simulations and models for shock induced plasticity.

The capillary fluctuation method (CFM) is widely used to compute solid–liquid interfacial properties from atomistic simulations, but its accuracy depends on choices in interface construction, wave-vector selection, sampling, and simulation geometry. Here, we develop a diagnostics-driven workflow for reproducible CFM calculations using pure Al as a representative system. Employing both ribbon models and thick two-dimensional references, we show that apparent linearity of the fluctuation spectrum alone does not ensure reliable stiffness or anisotropy estimates. Instead, a reliable CFM analysis requires a fitting window consistent with both temporal sampling and continuum capillary-wave assumptions, systematic sensitivity tests of the interface identification procedure, explicit propagation of replica variability, and independent verification of model-thickness convergence. We further propose a practical thickness-selection rule based on coexistence-temperature consistency, which enables finite-size effects to be controlled while retaining the substantial computational efficiency of ribbon geometries. By making the main sources of uncertainty explicit and diagnosable, the proposed workflow improves the reliability of the CFM as a quantitative tool and provides a foundation for its broader application to complex solid–liquid interfaces.

The impact of crystallographic orientation, select grain boundaries, and vacancies on the shock response of aluminum was investigated using molecular dynamics simulations. Shock loading in the [001], [011], and [111] directions was explored, revealing anisotropic behavior in shock speed, generated dislocation density, and melting. The Hugoniot elastic limit (HEL) in the [100], [110], and [111] directions were calculated as 18.7 GPa, 17.8 GPa, and 22.5 GPa, respectively. These results were found to be an order of magnitude larger than the uniaxial compressive yield strengths computed at high strain rate using an affine loading scheme. Metastable melting in the [011] and [111] directions occurred around a pressure of 100 GPa and roughly 1000 K below the observed metastable melting [001] direction and the equilibrium melt curve. The role of select twist and tilt grain boundaries was assessed. Differences in the wave speed profile were observed for most grain boundaries, but only for piston velocities [Formula: see text]1.5 km/s. Additionally, the presence of vacancies, both randomly distributed and clustered, led to a decrease in shock speed with a larger decrease in shock speed recorded at higher vacancy concentrations. The change in shock speed, relative to a defect free cell, exhibited a dependence on the configuration of the defects. Melting was also found to occur at lower pressures for increasing vacancy concentrations. These results highlight key trends in the role of defects and crystallographic orientation in the behavior of FCC metals, such as aluminum, under extreme loading conditions.

Fracture is fundamentally an atomic scale phenomenon. Grain-boundary structure, solute embrittlement, and grain-boundary defects can all impact crack propagation, but explicitly including atomic scale effects in models designed to study fracture at the micron or higher length scale is challenging. In contemporary phase-field fracture methods, it is common to assume an average critical energy release rate for a material domain; however, this approach has deleterious consequences for accuracy since crack propagation is not determined by local average toughness, but instead by a weakest link. We develop a phase-field fracture methodology where local critical energy release rates are input stochastically at grain boundaries in a polycrystal to implicitly include atomic scale effects. We perform multiple mode I fracture simulations to determine distributions of critical stress intensity Kc, strain energy density psi, and maximum tensile stress sigma max. The mean, standard deviation, initial crack position, and mesh size were varied to determine their effects. We generally found that the mean and variance of the output distribution of Kc correlated strongly with the mean and variance of the input distribution for critical energy release rate. Ultimately, in 83.8% of simulations, for reasonable expectations of the mean and variance of the critical energy release rate, locally stochastic fracture properties result in lower fracture toughness compared to models with uniform grain-boundary properties due to this weakest-link behavior. This validates the ability of the method to capture the extreme-value nature of fracture and demonstrates the importance of incorporating distributions of the critical energy release rate into simulations, not just the mean.

Despite its exceptional hardness, boron carbide suffers from a strength-limiting deformation mechanism called amorphization under high-pressure loading. The conditions governing amorphous band formation and their propagation remain unresolved. Using molecular dynamics simulations with a newly developed machine-learned interatomic potential, we investigate the influence of crystal orientation and polymorphism on the shock behavior of four boron carbide polymorphs. For the first time, shear-induced amorphous band formation is captured during shock compression, and its growth characteristics are systematically analyzed. We find that amorphous band growth varies with crystal orientation, with 0 degrees and 90 degrees chain alignments exhibiting increased susceptibility compared to 45 degrees and 60 degrees alignments. Crystal orientation also strongly affects the Hugoniot elastic limit (HEL), with chains parallel to the shock direction showing the lowest HEL, contradicting prior literature. Polymorphs with C-B-C chains exhibit enhanced toughness through chain bending and full recovery upon unloading, whereas those with C-C-C chains are prone to failure and cannot revert to their original structure. This behavior is linked to bonding tendencies between chain and icosahedral atoms. Furthermore, when amorphization leads to extensive crystal collapse, atomic packing in open spaces within the rhombohedral structure enables volume shrinkage, triggering fracture initiation. In contrast, localized amorphous band formation induces atomic rearrangement, leading to volume expansion and compressive stresses in the surrounding elastic matrix, aligning with previous predictions. These findings provide new insights into the dynamic response of boron carbide, shedding light on key factors that govern its deformation and failure mechanisms.

This study presents the application of constitutive artificial neural networks (CANNs) to model the flow stress and failure strain of steels under deformation, aiming to overcome key limitations of traditional constitutive models, such as the Johnson–Cook (JC) formulation. Two literature-based datasets are employed to train the CANNs: T24 steel for modeling the plastic flow stress and E250 steel for failure strain prediction. The results demonstrate substantial gains in predictive accuracy, with the CANN approach achieving a 75

Complex concentrated alloys (CCAs) are promising candidates for applications in extreme conditions, such as irradiation where interstitial mediated diffusion is important. In CCAs with N principal elements, N(N+1)2 types of dumbbell interstitials exist. Currently, there is no way to predict the thermal partition (fractional concentration at equilibrium) and the dynamic partition (fractional time an interstitial spends during diffusion) of each type of dumbbell interstitial. To mitigate this issue, this work proposes a theoretical model for computing the equilibrium concentrations and thermal partition of dumbbell interstitials in CCAs and validates the model using grand canonical Monte Carlo simulations. Lattice kinetic Monte Carlo simulations show that the thermal partition is equivalent to the dynamic partition, and both are governed by composition and formation energies of dumbbells. The model proposed provides a foundation for understanding radiation enhanced diffusion and induced segregation in CCAs under irradiation.

Nonequilibrium molecular dynamics simulations of shock loaded single-crystal Al in the < 100 >, < 110 >, < 111 >, and < 123 > orientations are conducted to study elastic and plastic shockwave formation and details associated with dislocation activity. A computer vision-based approach is implemented to capture the presence of dislocations and describe their spatial characteristics in the zone of nucleation behind the propagating shockwave. The methodology developed relies on the sequences of images extracted during shock loading that show dislocation activity within a cross section of the sample. Results reveal that the spacing between activated slip systems is orientation dependent and exhibits a modest reduction for the < 100 > and < 111 > orientations as shock pressure increases. Comparisons are made to existing theoretical models. Such relationships between shock pressure and dislocation activity, extracted from molecular dynamics simulations, can be used to inform higher length scale simulations or modeling of dislocation-based plasticity during shock.

Understanding the impact of helium bubbles on crack propagation is complex. A useful first study towards understanding bubble effects on fracture is to examine how voids impact fracture. In this work, we used phasefield fracture simulations to examine the influence of voids and their distribution on Mode I fracture in Fe. Assuming brittle fracture, two simulation configurations were considered: (1) nanoscale systems with one or two voids, and (2) nanoscale systems with an experimentally relevant distribution of voids, with up to 20 % void area. Results from simulations with one and two voids showed that voids within 10 nm of a crack tip reduce the stress required for crack growth, with the magnitude of reduction depending on void-to-crack orientation. Comparisons with linear elastic fracture mechanics and evaluation of one versus two void systems revealed deviations from linear superposition, implying complex interactions between void and crack tip stress fields. In multi-void simulations, as void sizes increase, the nearest void to the crack tip exerts a greater influence on fracture stress than the overall porosity. This study provides valuable insights into the relationship between void size and concentration, and the stress necessary for crack growth, marking a step forward towards understanding He bubble-induced fracture in ferrous materials.

We demonstrate a simple technique for improved imaging of non-crystallographic, irradiation-induced defects in the transmission electron microscope (TEM). By Fourier filtering bright-field TEM images to isolate relatively low-frequency fluctuations in intensity, it is possible to better resolve defects such as cavities caused by He-ion irradiation of iron. This analysis can be done with basic TEM imaging and most image processing software. This technique requires minimal defocus and can eliminate contrast mechanisms on both longer and short length scales. The resulting images are sensitive to filtering parameters, but clearly reveal the size, density, and distribution of cavities when compared with standard imaging techniques.

Equimolar CoCrNi is driven towards a long-range structure with transformation characteristics similar to that of a strain glass alloy due to the specific stoichiometry and applied aging conditions. This work illustrates the frustrated and kinetically arrested state of this alloy, which develops nano-size, single-phase, isostructural ordered domains at temperatures above 1273 K within a matrix of solid solution. Upon aging at lower temperatures, both atomistic simulation and TEM investigation demonstrate the chemical sensitivity of the matrix by localized symmetry changes which suppress any long-range transformation, mirroring the kinetics observed in strain-glass alloys. Careful quantification of experimental and simulated diffraction patterns from various aging conditions reveal the degree of order in CoCrNi to increase given longer aging times, with achievement of longer domain length scales only when subjected to temperatures below 873 K. This evidence indicates a kinetically constrained, chemically sensitive transition from a disordered fcc to a partially ordered, lower symmetry structure given adequate aging time and temperature. Magnetic effects on the transformation are dictated on the specific alloy stoichiometry and aging temperature, which act to amplify any effects of the glassy kinetics.

The number of published Machine Learning Interatomic Potentials (MLIPs) has increased significantly in recent years. These new data-driven potential energy approximations often lack the physics-based foundations that inform many traditionally-developed interatomic potentials and hence require robust validation methods for their accuracy, computational efficiency, and applicability to the intended applications. This work presents a sequential, three-stage workflow for MLIP validation: (i) preliminary validation, (ii) static property prediction, and (iii) dynamic property prediction. This material-agnostic procedure is demonstrated in a tutorial approach for the development of a robust MLIP for boron carbide (B4C), a widely employed, structurally complex ceramic that undergoes a deleterious deformation mechanism called ‘amorphization’ under high-pressure loading. It is shown that the resulting B4C MLIP offers a more accurate prediction of properties compared to the available empirical potential.

The strength and ductility of polycrystalline metals are influenced by the interactions between dislocations and grain boundaries, particularly when the grain size is small. One possible reaction is the absorption of dislocations into the grain boundary structure; however, the mobility of the resulting grain boundary dislocations (GBDs) remains largely unexplored. Thus, the objective of this work is to determine the mobility of GBDs arising from the absorption of lattice screw dislocations into sigma 3{111} and sigma 11{113} <110> symmetric tilt grain boundaries (STGBs) in Al. Atomistic simulations reveal a reduction in Peierls stress of similar to 80% and phonon damping coefficient of similar to 65% for GBDs in the {111}<110> STGB compared to lattice screw dislocations. This significant mobility increase is caused by the complete dissociation of the absorbed dislocation. The Peierls stress of GBDs in the {113}<110> STGB is increased to almost 8 times that of the lattice screw dislocation due to the smaller interplanar spacing between slip planes. This work provides a structural justification for the absorption reactions and demonstrates that the mobility of an absorbed dislocation is highly sensitive to the structure of the host grain boundary. Ultimately, mobility laws are provided which can be used to model GBD motion in mesoscale simulations.

A large body of work has been conducted to investigate the embrittlement and degradation of reactor pressure vessel (RPV) steels. This includes experiments on alloys with different compositions, performed in research and test reactors and ion accelerators spanning various temperatures, fluxes, and fluences. In this paper, we perform a critical review of the published experimental data and compile experimentally reported values for dislocation loop size/density, precipitate size/density and yield stress into an easily downloadable format that can be used by both experimentalists and modelers. This thorough experimental review is complemented by a brief review of simulation efforts at atomistic and mesoscopic length scales. This paper highlights key aspects of the behavior of RPV steels under irradiation, identifies gaps or discrepancies in current understandings, and identifies future priority research directions.

A linear regression-based machine learned interatomic potential (MLIP) was developed for the silicon-carbon system. The MLIP was predominantly trained on structures discovered through a genetic algorithm, encompassing the entire silicon-carbon composition space, and uses as its foundation the Ultra-Fast Force Fields (UF3) formulation. To improve MLIP performance, the learning algorithm was modified to include higher spline interpolation resolution in regions with large potential energy surface curvature. The developed MLIP demonstrates exceptional predictive performance, accurately estimating energies and forces for structures across the silicon-carbon composition and configuration space. The MLIP predicts mechanical properties of SiC with high precision and captures fundamental volume-pressure and volume-temperature relationships. Uniquely, this silicon-carbon MLIP is adept at modeling complex high-temperature phenomena, including the peritectic decomposition of SiC and carbon dimer formation during SiC surface reconstruction, which cannot be captured with prior classical interatomic potentials for this material.

Junctions are discontinuities in flat grain boundaries that arise in all polycrystalline materials and are thought to play important roles in the response of a grain boundary network to thermal and mechanical loads. A key open question concerns the mechanisms by which solute segregation to junctions impacts properties of the grain boundary. In this work, we investigate the influence of grain boundary facet junctions on solute embrittlement, and we present an analytical model that uses the hydrostatic stress field contributed by dislocations at multiple junctions to describe these effects. Specifically, we study junctions between {112} facets of various lengths in Au 〈111〉Σ3 tilt grain boundaries. Copper and silver solutes are employed to determine if the effect of junctions on solute segregation and embrittlement is dependent on size relative to the host. Combined, atomistic simulation data and the analytical model show that Cu and Ag have opposite segregation responses to junctions due to the sign of the hydrostatic stress field induced by junctions. However, a positive shift in the embrittling potency is computed near junctions regardless of solute type or the stress state of the segregation site. Hence, for the conditions studied, junctions consistently shift the energetic landscape towards embrittlement.

A phase-field model is parameterized to study the effect of elastic stresses on the migration of He gas bubbles in Fe under a temperature gradient. Stresses caused by the gas bubble pressure and residual stress in the Fe matrix are considered. The dependence of He bubble migration velocity on the magnitude of the residual stress, average temperature, temperature gradient, and bubble size is measured. In agreement with a theoretical model based on surface diffusion, simulation results demonstrate that He bubbles move towards the high temperature region with velocities in Fe that are orders of magnitude faster than previously reported in UO2. It is found that local stresses in the matrix caused by the He bubble have negligible effect on the bubble migration process; however, residual stresses in the Fe matrix, potentially caused by processing or irradiation, can modestly modify bubble kinetics through pressure dependence of the He diffusion coefficients. Compressive residual stress decreases diffusion coefficients for bulk and surface diffusion mechanisms, thus reducing the migration velocity of the gas bubble. In contrast, tensile residual stress increases the diffusion coefficients, resulting in an increase in the gas bubble migration velocity. This pressure dependence is also consistent with a theoretical model. This phase field model lays the foundation for analysis of bubble coalescence-induced fracture in He bubble-containing steels.

Steels are used extensively in many industries due to their excellent balance of mechanical strength, manufacturability, costs, and acceptable corrosion resistance. Steels are being considered for structural components that are exposed to particularly challenging environments in advanced nuclear reactors. In this context, force-field molecular dynamics (MD) simulations were performed on austenitic steel surrogates with Fe40Cr25Ni35 and Fe50Cr20Ni30 compositions to compute the lattice parameter, elastic constants, bulk modulus, Poisson ratio, the dislocation velocity as a function of shear stress, and the phonon drag coefficient for use in discrete dislocation dynamics (DDD) simulations. The accurate computation of these parameters is essential to obtain correct results by the evolution of the dislocation network of the materials using DDD simulations. The dislocation velocity was extracted from the MD calculations for both steel compositions at 30 different values of stress. Because no significant migration of dislocations was observed at stresses below 200 MPa, the dislocation velocity and mobility were calculated at stresses of 200 MPa and higher. Subsequently, the dislocation density and strain–stress relationship were then computed using the DDD approach. The elastic and plastic deformations in the Fe40Cr25Ni35 system were found to be considerably larger than those of the Fe50Cr20Ni30 system. Our study illustrates the ability of atomistic and dislocation dynamics simulations to elucidate qualitative descriptions of the elasticity and plasticity in steel materials, and thus can assist experimental efforts to evaluate the impact of deformation in austenitic steels.