Application of machine learning potentials to predict grain boundary properties in fcc elemental metals
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DOI: 10.1103/physrevmaterials.4.123607
Abstract: Accurate interatomic potentials are in high demand for large-scale atomistic simulations of materials that are prohibitively expensive by density functional theory (DFT) calculation. In this study, we apply machine learning potentials in a recently constructed… read more here.
Keywords: grain boundary; machine learning; elemental metals; grain ... See more keywords