2019/07/21 by Hui Zheng, Xiangguo Li, Xiang-Guo Li +16 · 4 citations
Engineering · Materials Science · Physics and Astronomy · #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Metal and Thin Film Mechanics #Microstructure and mechanical properties #cond-mat.mtrl-sci
paper · pdf · doi:10.48550/arxiv.1907.08905
32 pages, 8 figures, 2 tables in main manuscript; 6 figures, 5 tables, in 14 pages of Supplementary Information
arxiv created 2019/07/21 · openalex publication_date 2019/07/21 · arxiv updated 2019/07/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The structure and energy of grain boundaries (GBs) are essential for predicting the properties of polycrystalline materials. In this work, we use high-throughput density functional theory calculations workflow to construct the Grain Boundary Database (GBDB), the largest database of DFT-computed grain boundary properties to date. 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. In particular, we demonstrate a novel scaled-structural template approach for HT GB calculations, which reduces the computational cost of converging GB structures by a factor of ∼ 3-6. The grain boundary energies and work of separation are rigorously validated against previous experimental and computational data. Using this large GB dataset, we develop an improved predictive model for the GB energy of different elements based on the cohesive energy and shear modulus. The open GBDB represent a significant step forward in the availability of first principles GB properties, which we believe would help guide the future design of polycrystalline materials.