A Universal Machine Learning Model for Elemental Grain Boundary Energies

Journal Article · 12 May 2022 · Scripta Materialia

DOI: https://doi.org/10.1016/j.scriptamat.2022.114803 · OSTI ID:1981754

Ye, Weike ^[1]; Zheng, Hui  ^[1]; Chen, Chi  ^[1]; Ong, Shyue Ping  ^[1]

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1. Univ. of California, San Diego, CA (United States)

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.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1981754
• Journal Information: Scripta Materialia, Journal Name: Scripta Materialia Vol. 218; ISSN 1359-6462
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English