Site occupancy behavior of the binary μ phase

Machine Learning-Aided High-Throughput First-Principles Calculations to Predict the Formation Energy of μ Phase

The μ phase is a type of hard and brittle constituent that exists in high-temperature alloys. The formation energy is a crucial thermochemical datum, and the accurate calculation of the formation energy of the μ phase contributes to the material design of high-temperature alloys. Traditional first-principles calculations demand significant computational time and resources. In this study, an innovative machine learning (ML)-based approach to accurately predict the formation energy of the μ phase is proposed. This approach involves the utilization of six algorithms and two model evaluation methods to construct the ML models. Leveraging a comprehensive data set containing 1036 binary configurations of the μ phase, the model trained using a 10-fold cross-validation technique, and the multilayer perceptron (MLP) algorithm achieves a mean absolute error (MAE) of 23.906 meV/atom. To validate its generalization performance, the trained model is further validated on 900 ternary configurations, resulting in an MAE of 32.754 meV/atom. Compared with solely using traditional first-principles calculations, our approach significantly reduces the computational time by at least 52%. Moreover, the ML model exhibits exceptional accuracy in predicting the lattice parameters of the μ phase. The MAE values for the a and c parameters are 0.024 and 0.214 Å, respectively, corresponding to low error rates of only 0.479 and 0.578%. Additionally, the ML model was utilized to accurately predict the formation energy of all of the possible ternary configurations. To enhance accessibility to the formation energy data of the μ phase, a user-friendly graphical user interface (GUI) was developed, ensuring convenient usability for researchers and practitioners.

Tailoring the Plasticity of Topologically Close-Packed Phases via the Crystals' Fundamental Building Blocks

Brittle topologically close-packed precipitates form in many advanced alloys. Due to their complex structures, little is known about their plasticity. Here, a strategy is presented to understand and tailor the deformability of these complex phases by considering the Nb-Co µ-phase as an archetypal material. The plasticity of the Nb-Co µ-phase is controlled by the Laves phase building block that forms parts of its unit cell. It is found that between the bulk C15-NbCo₂ Laves and Nb-Co µ-phases, the interplanar spacing and local stiffness of the Laves phase building block change, leading to a strong reduction in hardness and stiffness, as well as a transition from synchroshear to crystallographic slip. Furthermore, as the composition changes from Nb₆ Co₇ to Nb₇ Co₆ , the Co atoms in the triple layer are substituted such that the triple layer of the Laves phase building block becomes a slab of pure Nb, resulting in inhomogeneous changes in elasticity and a transition from crystallographic slip to a glide-and-shuffle mechanism. These findings open opportunities to purposefully tailor the plasticity of these topologically close-packed phases in the bulk by manipulating the interplanar spacing and local shear modulus of the fundamental crystal building blocks at the atomic scale.