2022/01/28 by Weike Ye, Ye, Weike, Hui Zheng +5 · 1 citation
Computer Science · Materials Science · Mathematics · Physics and Astronomy · #Artificial intelligence #Boundary (topology) #Computational Physics (physics.comp-ph) #Computer science #Crystallite #Domain (mathematical analysis) #Electron and X-Ray Spectroscopy Techniques #Energy (signal processing) #FOS: Physical sciences #Geometry #Grain boundary #Machine Learning in Materials Science #Machine learning #Materials Science (cond-mat.mtrl-sci) #Materials science #Mathematical analysis #Mathematics #Mean absolute error #Mean squared error #Metallurgy #Physics #Quantum mechanics #Scaling #Sigma #Software Engineering Research #Statistical physics #Statistics #cond-mat.mtrl-sci #physics.comp-ph
paper · pdf · doi:10.48550/arxiv.2201.11991
published in arXiv (Cornell University) (Cornell University) · 12 pages, 4 figures
arxiv created 2022/01/28 · openalex publication_date 2022/01/28 · arxiv updated 2022/01/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
The grain boundary (GB) energy has a profound influence on the grain growth and properties of polycrystalline metals. Here, we show that the energy of a GB, normalized by the bulk cohesive energy, can be described purely by four geometric features. By machine learning on a large computed database of 361 small Σ (Σ< 10) GBs of more than 50 metals, we develop a model that can predict the grain boundary energies to within a mean absolute error of 0.13 J m-2. More importantly, this universal GB energy model can be extrapolated to the energies of high Σ GBs without loss in accuracy. These results highlight the importance of capturing fundamental scaling physics and domain knowledge in the design of interpretable, extrapolatable machine learning models for materials science.