No abstract

Conventional models for grain growth are based on the assumption that grain boundary (GB) velocity is proportional to GB mean curvature. We demonstrate via a series of molecular dynamics (MD) simulations that such a model is inadequate and that many physical phenomena occur during grain boundary migration for which this simple model is silent. We present a series of MD simulations designed to unravel GB migration phenomena and set it in a GB migration context that accounts for competing migration mechanisms, elasticity, temperature, and grain boundary crystallography. The resultant formulation is quantitative and validated through a series of atomistic simulations. The implications of this model for microstructural evolution is described. We show that consideration of GB migration mechanisms invites considerable complexity even under ideal conditions. However, that complexity also grants these systems enormous flexibility, and that flexibility is key to the decades-long success of conventional grain growth theories.

In polycrystalline materials, grain boundaries are sites of enhanced atomic motion, but the complexity of the atomic structures within a grain boundary network makes it difficult to link the structure and atomic dynamics. Here, we use a machine learning technique to establish a connection between local structure and dynamics of these materials. Following previous work on bulk glassy materials, we define a purely structural quantity (softness) that captures the propensity of an atom to rearrange. This approach correctly identifies crystalline regions, stacking faults, and twin boundaries as having low likelihood of atomic rearrangements while finding a large variability within high-energy grain boundaries. As has been found in glasses, the probability that atoms of a given softness will rearrange is nearly Arrhenius. This indicates a well-defined energy barrier as well as a well-defined prefactor for the Arrhenius form for atoms of a given softness. The decrease in the prefactor for low-softness atoms indicates that variations in entropy exhibit a dominant influence on the atomic dynamics in grain boundaries.

We present the results of large-scale molecular dynamics simulations of grain growth in polycrystalline nickel with nanoscale grains. The simulations show that grain growth is accompanied by coherent twin boundary (CTB) generation. As the grains grow, twins collide; such collisions result in twin junctions. We catalog all possible twin junctions and show examples of each from the simulations. These include junctions of 2-4 CTBs with grain boundaries and five-fold twin junctions (penta-twins). We elucidate the mechanisms by which all of these junctions form and their relative frequencies. Penta-twins, which are rare in coarse microstructures, occur frequently in nanocrystalline metals. Their absence in macro-scale samples can be traced to the wedge-disclination character (and, consequently, an elastic energy that diverges with sample size). In the nanocrystalline case, the presence of penta-twins can be traced to this twin collision formation mechanism, which is responsible for their wedge-disclination dipole character (relatively small elastic energy). We demonstrate how all CTB junctions, especially penta-twins, retard grain growth.

Shear coupling implies that all grain boundary (GB) migration necessarily creates mechanical stresses/strains and is a key component to the evolution of all polycrystalline microstructures. We present MD simulation data and theoretical analyses that demonstrate the GB shear coupling is not an intrinsic GB property, but rather strongly depends on the type and magnitude of the driving force for migration and temperature. We resolve this apparent paradox by proposing a microscopic theory for GB migration that is based upon a statistical ensemble of line defects (disconnections) that are constrained to lie in the GB. Comparison with the MD results for several GBs provides quantitative validation of the theory as a function of stress, chemical potential jump and temperature. arXiv:1810.04647v1 [cond-mat.mtrl-sci]