Calculation of proper energy barriers for atomistic kinetic Monte Carlo simulations on rigid lattice with chemical and strain field long-range effects using artificial neural networks

Deformation mechanisms in high entropy alloys: a minireview of short-range order effects

The complex atomic scale structure of high entropy alloys presents new opportunities to expand the deformation theories of mechanical metallurgy. In this regard, solute-defect interactions have emerged as critical piece in elucidating the operation of deformation mechanisms. While notable progress has been made in understanding solute-defect interactions for random solute arrangements, recent interest in high entropy alloys with short-range order adds a new layer of structural complexity for which a cohesive picture has yet to emerge. To this end, this minireview synthesizes the current understanding of short-range order effects on defect behavior through an examination of the key recent literature. This analysis centers on the nanoscale metallurgy of deformation mechanisms, with the order-induced changes to the relevant defect energy landscapes serving as a touchstone for discussion. The topics reviewed include dislocation-mediated strengthening, twinning and phase transformation-based mechanisms, and vacancy-mediated processes. This minireview concludes with remarks on current challenges and opportunities for future efforts.

Active sampling for neural network potentials: Accelerated simulations of shear-induced deformation in Cu-Ni multilayers

Neural network potentials (NNPs) can greatly accelerate atomistic simulations relative to ab initio methods, allowing one to sample a broader range of structural outcomes and transformation pathways. In this work, we demonstrate an active sampling algorithm that trains an NNP that is able to produce microstructural evolutions with accuracy comparable to those obtained by density functional theory, exemplified during structure optimizations for a model Cu-Ni multilayer system. We then use the NNP, in conjunction with a perturbation scheme, to stochastically sample structural and energetic changes caused by shear-induced deformation, demonstrating the range of possible intermixing and vacancy migration pathways that can be obtained as a result of the speedups provided by the NNP. The code to implement our active learning strategy and NNP-driven stochastic shear simulations is openly available at https://github.com/pnnl/Active-Sampling-for-Atomistic-Potentials.

A novel method for calculating the energy barriers for carbon diffusion in ferrite under heterogeneous stress

A novel method for accurate and efficient evaluation of the change in energy barriers for carbon diffusion in ferrite under heterogeneous stress is introduced. This method, called Linear Combination of Stress States, is based on the knowledge of the effects of simple stresses (uniaxial or shear) on these diffusion barriers. Then, it is assumed that the change in energy barriers under a complex stress can be expressed as a linear combination of these already known simple stress effects. The modifications of energy barriers by either uniaxial traction/compression and shear stress are determined by means of atomistic simulations with the Climbing Image-Nudge Elastic Band method and are stored as a set of functions. The results of this method are compared to the predictions of anisotropic elasticity theory. It is shown that, linear anisotropic elasticity fails to predict the correct energy barrier variation with stress (especially with shear stress) whereas the proposed method provides correct energy barrier variation for stresses up to ∼3 GPa. This study provides a basis for the development of multiscale models of diffusion under non-uniform stress.

On the mobility of vacancy clusters in reduced activation steels: an atomistic study in the Fe-Cr-W model alloy

Reduced activation steels are considered as structural materials for future fusion reactors. Besides iron and the main alloying element chromium, these steels contain other minor alloying elements, typically tungsten, vanadium and tantalum. In this work we study the impact of chromium and tungsten, being major alloying elements of ferritic Fe-Cr-W-based steels, on the stability and mobility of vacancy defects, typically formed under irradiation in collision cascades. For this purpose, we perform ab initio calculations, develop a many-body interatomic potential (EAM formalism) for large-scale calculations, validate the potential and apply it using an atomistic kinetic Monte Carlo method to characterize the lifetime and diffusivity of vacancy clusters. To distinguish the role of Cr and W we perform atomistic kinetic Monte Carlo simulations in Fe-Cr, Fe-W and Fe-Cr-W alloys. Within the limitation of transferability of the potentials it is found that both Cr and W enhance the diffusivity of vacancy clusters, while only W strongly reduces their lifetime. The cluster lifetime reduction increases with W concentration and saturates at about 1-2 at.%. The obtained results imply that W acts as an efficient 'breaker' of small migrating vacancy clusters and therefore the short-term annealing process of cascade debris is modified by the presence of W, even in small concentrations.

Correlated recombination and annealing of point defects in dilute and concentrated Fe-Cr alloys

In this work, we present a comprehensive combined modelling approach to study the annealing of lattice defects in dilute and concentrated metallic alloys. The developed approach consists in the combination of molecular dynamics, atomistic kinetic Monte Carlo (AKMC) and mean field rate theory methods, linked at appropriate time and space scales. For the first time, the AKMC tool has been designed to model the evolution of point defects (both vacancies and self-interstitial atoms) in random concentrated alloys, taking into account the influence of lattice distortion on the local migration energy barrier due to the mutual interaction of point defects and solutes. Good accuracy and outstanding speed of calculations has been achieved by introducing the artificial neural network regression as an engine of the AKMC applied to calculate migration barriers for mobile defects. The developed method was applied to study correlated recombination in bcc Fe and random Fe-Cr alloys, aiming at the reproduction of a set of experimental studies after electron irradiation. The obtained results agree well with the available experimental data, implying that the developed modelling procedure correctly captures the undergoing physical process.

Markov-chain model of classified atomistic transition states for discrete kinetic Monte Carlo simulations

Classical harmonic transition state theory is considered and applied in discrete lattice cells with hierarchical transition levels. The scheme is then used to determine transitions that can be applied in a lattice-based kinetic Monte Carlo (KMC) atomistic simulation model. The model results in an effective reduction of KMC simulation steps by utilizing a classification scheme of transition levels for thermally activated atomistic diffusion processes. Thermally activated atomistic movements are considered as local transition events constrained in potential energy wells over certain local time periods. These processes are represented by Markov chains of multidimensional Boolean valued functions in three-dimensional lattice space. The events inhibited by the barriers under a certain level are regarded as thermal fluctuations of the canonical ensemble and accepted freely. Consequently, the fluctuating system evolution process is implemented as a Markov chain of equivalence class objects. It is shown that the process can be characterized by the acceptance of metastable local transitions. The method is applied to a problem of Au and Ag cluster growth on a rippled surface. The simulation predicts the existence of a morphology-dependent transition time limit from a local metastable to stable state for subsequent cluster growth by accretion. Excellent agreement with observed experimental results is obtained.