Fig. 4: The predicted single-step evolution for various dislocation configurations and learning method.
From: Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning
a PI-GNN mobility law is trained on coarse-grained dipole dataset with different RSS values, temperatures, and angles between Burger’s vector and tangent vector. The predictions are also made on dipole configurations with different line orientations at 500 K and 0.9 GPa. b PI-GNN mobility law trained on dislocation loop expansion data and prediction on dislocation loops at 500 K and 0.9, 1.2, and 1.5 GPa. c Testing the generalizability of the PI-GNN mobility law by training on dipole dataset and predicting on loop expansion.
