Extended Shear Deformation of the Immiscible Cu-Nb Alloy Resulting in Nanostructuring and Oxygen Ingress with Enhancement in Mechanical Properties.

Deformation processing of immiscible systems is observed to disrupt thermodynamic equilibrium, often resulting in nonequilibrium microstructures. The microstructural changes including nanostructuring, hierarchical distribution of phases, localized solute supersaturation, and oxygen ingress result from high-strain extended deformation, causing a significant change in mechanical properties. Because of the dynamic evolution of the material under large strain shear load, a detailed understanding of the transformation pathway has not been established. Additionally, the influence of these microstructural changes on mechanical properties is also not well characterized. Here, an immiscible Cu-4 at. % Nb alloy is subjected to a high-strain shear deformation (∼200); the deformation-induced changes in the morphology, crystal structure, and composition of Cu and Nb phases as a function of total strain are characterized using transmission electron microscopy and atom probe tomography. Furthermore, a multimodal experiment-guided computational approach is used to depict the initiation of deformation by an increase in misorientation boundaries by crystal plasticity-based grain misorientation modeling (strain ∼0.6). Then, co-deformation and nanolamination of Cu and Nb are envisaged by a finite element method-based computational fluid dynamic model with strain ranging from 10 to 200. Finally, the experimentally observed amorphization of the severely sheared supersaturated Cu-Nb-O phase was validated using the first principle-based simulation using density functional theory while highlighting the influence of oxygen ingress during deformation. Furthermore, the nanocrystalline microstructure shows a >2-fold increase in hardness and compressive yield strength of the alloy, elucidating the potential of deformation processing to obtain high-strength low-alloyed metals. Our approach presents a step-by-step evolution of a microstructure in an immiscible alloy undergoing severe shear deformation, which is broadly applicable to materials processing based on friction stir, extrusion, rolling, and surface shear deformation under wear and can be directly applied to understanding material behavior during these processes.

Self-learning kinetic Monte Carlo simulations of diffusion in ferromagnetic α-Fe-Si alloys.

Diffusion of Si atom and vacancy in the A2-phase of α-Fe-Si alloys in the ferromagnetic state, with and without magnetic order and in various temperature ranges, are studied using AKSOME, an on-lattice self-learning KMC code. Diffusion of the Si atom and the vacancy are studied in the dilute limit and up to 12 at.% Si, respectively, in the temperature range 350-700 K. Local Si neighborhood dependent activation energies for vacancy hops were calculated on-the-fly using a broken-bond model based on pairwise interaction. The migration barrier and prefactor for the Si diffusion in the dilute limit were obtained and found to agree with published data within the limits of uncertainty. Simulations results show that the prefactor and the migration barrier for the Si diffusion are approximately an order of magnitude higher, and a tenth of an electron-volt higher, respectively, in the magnetic disordered state than in the fully ordered state. However, the net result is that magnetic disorder does not have a significant effect on Si diffusivity within the range of parameters studied in this work. Nevertheless, with increasing temperature, the magnetic disorder increases and its effect on the Si diffusivity also increases. In the case of vacancy diffusion, with increasing Si concentration, its diffusion prefactor decreases while the migration barrier more or less remained constant and the effect of magnetic disorder increases with Si concentration. Important vacancy-Si/Fe atom exchange processes and their activation barriers were identified, and the effect of energetics on ordered phase formation in Fe-Si alloys are discussed.

Self-learning kinetic Monte Carlo simulations of Al diffusion in Mg.

Vacancy-mediated diffusion of an Al atom in the pure Mg matrix is studied using the atomistic, on-lattice self-learning kinetic Monte Carlo (SLKMC) method. Activation barriers for vacancy-Mg and vacancy-Al atom exchange processes are calculated on the fly using the climbing image nudged-elastic-band method and binary Mg-Al modified embedded-atom method interatomic potential. Diffusivities of an Al atom obtained from SLKMC simulations show the same behavior as observed in experimental and theoretical studies available in the literature; that is, an Al atom diffuses faster within the basal plane than along the c-axis. Although the effective activation barriers for an Al atom diffusion from SLKMC simulations are close to experimental and theoretical values, the effective prefactors are lower than those obtained from experiments. We present all the possible vacancy-Mg and vacancy-Al atom exchange processes and their activation barriers identified in SLKMC simulations. A simple mapping scheme to map an HCP lattice onto a simple cubic lattice is described, which enables simulation of the HCP lattice using the on-lattice framework. We also present the pattern recognition scheme which is used in SLKMC simulations to identify the local Al atom configuration around a vacancy.