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.
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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.