Temperature dependent stacking fault free energy profiles and partial dislocation separation in FCC Cu
Résumé
The generalized stacking fault energy profile is fundamental to models of metal plasticity and thus a key parameter for alloy design. However, to account for thermal vibrations, models require the stacking fault free energy profile, but current methods can only calculate metastable intrinsic stacking faults. We show how the full stacking fault free energy profile can be calculated using PAFI, a linear scaling method that fully accounts for anharmonic thermal vibrations. Applying our approach to empirical and machine learning potentials for FCC Cu, we show via direct comparison with molecular dynamics simulations that accounting for temperature effects is essential to predict the statistics of partial dislocation separations. The machine learning potential gives quantitative agreement with available density functional theory data on the intrinsic stacking fault energy, whilst additionally returning the unstable stacking fault, a key parameter for predicting ductile fracture.