Origin of the bismuth-induced decohesion of nickel and copper grain boundaries

Stabilization and Self-Passivation of Grain Boundaries in Halide Perovskite by Rigid Body Translation

The physical properties of grain boundaries in halide perovskites, especially their atomic structure, have not been fully understood yet. We report that Σ5 [130] symmetrical tilt grain boundaries can be stabilized by rigid body translation which is moving one side of the grain parallel with respect to the adjacent grain. Such reconstruction passivates grain boundaries by removing Pb-Pb and I-I interactions that introduce shallow defect states in the band gap. Rigid body translation also stabilizes the [110] antiphase boundary as well in both CsPbI₃ and CsPbBr₃.

Understanding solute effect on grain boundary strength based on atomic size and electronic interaction

Solute segregating to grain boundary can stabilize the microstructure of nanocrystalline materials, but a lot of solutes also cause embrittlement effect on interfacial strength. Therefore, uncovering the solute effect on grain boundary strength is very important for nanocrystalline alloys design. In this work, we have systematically studied the effects of various solutes on the strength of a Σ5 (310) grain boundary in Cu by first-principle calculations. The solute effects are closely related to the atomic radius of solutes and electronic interactions between solutes and Cu. The solute with a larger atomic radius is easier to segregate the grain boundary but causes more significant grain boundary embrittlement. The weak electronic interactions between the s- and p-block solutes and Cu play a very limited role in enhancing grain boundary strength. While the strong d-states electronic interactions between transition metallic solutes and Cu can counteract embrittlement caused by size mismatch and significantly improve the grain boundary strength. This work deepens our understanding of solute effects on grain boundary strength based on atomic size and electronic interactions.

Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects

The strengthening energy or embrittling potency of an alloying element is a fundamental energetics of the grain boundary (GB) embrittlement that control the mechanical properties of metallic materials. A data-driven machine learning approach has recently been used to develop prediction models to uncover the physical mechanisms and design novel materials with enhanced properties. In this work, to accurately predict and uncover the key features in determining the strengthening energies, three machine learning methods were used to model and predict strengthening energies of solutes in different metallic GBs. In addition, 142 strengthening energies from previous density functional theory calculations served as our dataset to train three machine learning models: support vector machine (SVM) with linear kernel, SVM with radial basis function (RBF) kernel, and artificial neural network (ANN). Considering both the bond-breaking effect and atomic size effect, the nonlinear kernel based SVR model was found to perform the best with a correlation of r2 ~ 0.889. The size effect feature shows a significant improvement to prediction performance with respect to using bond-breaking effect only. Moreover, the mean impact value analysis was conducted to quantitatively explore the relative significance of each input feature for improving the effective prediction.