Abstract
We develop the Gaussian process regression model to shed light on relationships between metal surface energy and pertinent physical parameters. A total of 43 metals with surface energy ranging from 0.10 to 3.68 Jm−2 are explored for this purpose. The dataset contains alkali, alkaline earth, and transition metals, Lanthanides, and metals in other groups with the face-centered cubic, body-centered cubic, or hexagonal-closed-packed structure. The model is accurate and stable that contributes to fast estimations of surface energy of various metals at low cost.
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