GNNs can replace expensive electronic calculations for simulating spin dynamics in magnets by learning effective magnetic force fields, similar to how machine-learned potentials work for atomic systems.
Researchers developed a graph neural network framework that learns to predict magnetic forces in metallic magnets directly from electronic calculations. This approach eliminates expensive repeated electronic simulations during time evolution, enabling fast and accurate predictions of spin dynamics across different magnetic structures.