Polycrystalline materials are composed of many grains, and the interface between grains is called a grain boundary. The structure and energy of grain boundaries are essential for predicting the properties of polycrystalline materials. Therefore, scientists are committed to studying the properties of grain boundaries to better understand and design polycrystalline materials.
Methods
Fig. 1. Grain boundary generation process.
Constructed the largest database of DFT-computed grain boundary properties to date, GBDB. The database currently encompasses 327 GBs of 58 elemental metals, including 10 common twist or symmetric tilt GBs for body-centered cubic (bcc) and face-centered cubic (fcc) systems and the Σ7 [0001] twist GB for hexagonal close-packed (hcp) systems.
Developed a novel scaled-structural template approach for high-throughput grain boundary calculations, significantly reducing the computational cost.
Developed an improved predictive model for the GB energy of different elements, improving the prediction accuracy.
Provides more accurate grain boundary property data for the design of polycrystalline materials.
Promotes the application of high-throughput DFT calculations in materials science.
Provides an improved direction for the predictive model of grain boundary properties.
Fig. 3. Comparison of γGB between the following: (a) this work and previous DFT values; (b) and (c) EAM and SNAP values. (d), (e) and (f) compare the calculated γGB of bcc Fe, fcc Al and fcc Ni with experimentally measured MRD. Where: γGB refers to grain boundary energy; DFT refers to density functional theory; EAM refers to embedded atom method; SNAP refers to spectral neighbor analysis potentials; MRD refers to multiples of random distribution.
