Fig. 5: The predicted single-step error for various dislocation configurations and learning method.
From: Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning
PI-GNN mobility law is trained on coarse-grained dipole dataset with different applied stress levels, temperatures, and angles between Burger’s vector and tangent vector. The predictions are also made on dipole configurations with different orientations at 500 K and 0.9 GPa. Both the (a) percentage error and (b) absolute unnormalized error are shown. c PI-GNN mobility law trained on dislocation loop expansion data and prediction on dislocation loops at 500 K and 700 K and various applied stress levels. d Testing the generalizability of the PI-GNN mobility law by training on dipole dataset and predicting on loop expansion.
