Segregation-induced ordered superstructures at general grain boundaries in a nickel-bismuth alloy

Abstract

The properties of materials change, sometimes catastrophically, as alloying elements and impurities accumulate preferentially at grain boundaries. Studies of bicrystals show that regular atomic patterns often arise as a result of this solute segregation at high-symmetry boundaries, but it is not known whether superstructures exist at general grain boundaries in polycrystals. In bismuth-doped polycrystalline nickel, we found that ordered, segregation-induced grain boundary superstructures occur at randomly selected general grain boundaries, and that these reconstructions are driven by the orientation of the terminating grain surfaces rather than by lattice matching between grains. This discovery shows that adsorbate-induced superstructures are not limited to special grain boundaries but may exist at a variety of general grain boundaries, and hence they can affect the performance of polycrystalline engineering alloys.

Genetic algorithm-guided deep learning of grain boundary diagrams: Addressing the challenge of five degrees of freedom

“…Third, the GA found that denser (lower Miller index) terminating grain plane is more important to control adsorption and structures of asymmetric AT and MX GBs, as well as all-included GBs. Notably, this finding is again consistent with recent advanced microscopy observations that lower-index grain surface dictates faceting and segregation behaviors [16,44].…”

“…We primarily adopt the latter, where GBs are classified into four groups: ST, TW, AT, and MX GBs ( Fig. 2a) [16]. Moreover, we identified and summarized 38 commonly used GB descriptors in Table S2 in SM, including both structural and geometrical parameters.…”

“…S8 in SM). We used two large Σ values (99 and 599) in the DNN predictions to extrapolate (noting that Σ > 500 represents sufficiently general GBs [16]) to Σ→∞.…”

“…Recently, various advanced computational methods, such as the evolutionary algorithm [21], genetic algorithm (GA) [8,22], Bayesian optimization [23], and machine learning (ML) [24][25][26], combined with high-throughput calculations [27], data mining [28], and virtual screening [29], have been used to model GBs; however, most of these studies focused on either symmetric-tilt or twist GBs. More general and asymmetric GBs [16], which are ubiquitous in polycrystals and often the weak links chemically and mechanically, are hitherto scarcely investigated by virtually any modeling method. Understanding the GB properties as functions of seven DOFs (five crystallographic DOFs plus bulk composition and temperature) remains a grand challenge, which motivated this study.…”

Review of grain boundary complexion engineering: Know your boundaries

“…Finally, complexions cannot have three‐dimensional periodicity in the true sense (ie, as in bulk crystalline phases) because as interfaces they are quasi‐two dimensional structures. In short, complexions have two‐dimensional periodicity parallel to the interface and are quasi‐periodic for a short distance in the direction perpendicular to the interface, even at general grain boundaries …”