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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… Click to show full abstract
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 J m − 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.
Journal Title: Materials Chemistry and Physics
Year Published: 2021
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