A regression model trained on interatomic-potential data predicts the same grain boundary energy scaling from fundamental properties that density functional theory finds, supporting the use of potential ensembles as synthetic materials.
The average atomistic GB energy from OpenKIM (solid black) is compared with the average best fit analytical LM model (dashed green)
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Fundamental Microscopic Properties as Predictors of Large-Scale Quantities of Interest: Validation through Grain Boundary Energy Trends
A regression model trained on interatomic-potential data predicts the same grain boundary energy scaling from fundamental properties that density functional theory finds, supporting the use of potential ensembles as synthetic materials.