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- W4280491371 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." @default.
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- W4280491371 date "2022-09-01" @default.
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- W4280491371 title "A Universal Machine Learning Model for Elemental Grain Boundary Energies" @default.
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- W4280491371 doi "https://doi.org/10.1016/j.scriptamat.2022.114803" @default.
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