A Universal Machine Learning Model for Elemental Grain Boundary Energies. (September 2022)

• Record Type: Journal Article
• Authors: Ye, Weike; Zheng, Hui; Chen, Chi; Ong, Shyue Ping
• Is Part Of: Scripta materialia. Number 218(2022)
• Publication Date: 2022-09
• Subjects: Grain boundary energy -- Modeling -- Density Functional Theory (DFT) -- Machine learning; Materials -- Periodicals; Metallurgy -- Periodicals; Metalen; Legeringen; Materiaalkunde; Metals, metalworking and machinery industries; Metals; Electronic journals; 620.11
• DOI: 10.1016/j.scriptamat.2022.114803
• Languages: English
• ISSNs: 1359-6462

Abstract

Graphical abstract: Abstract: 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.