# Spin-Lattice Dynamics

A magnetic neuroevolution potential takes magnetic moments as inputs alongside atomic positions, so different magnetic orders of the same atomic structure no longer share one energy surface. Trained on the same datasets for the same number of iterations as a standard neuroevolution potential without magnetic descriptors, it cuts energy root-mean-square error by up to about one order of magnitude on CrN, Fe and FeGe. Spin-lattice simulations of Fe give a magnetic transition temperature near 1000 K, close to the experimental Curie temperature of 1043 K, and one consumer-grade GPU handles coupled dynamics of more than two million atoms.

A magnetic neuroevolution potential takes magnetic moments as inputs alongside atomic positions, so different magnetic orders of the same atomic structure no longer share one energy surface. Trained on the same datasets for the same number of iterations as a standard neuroevolution potential without magnetic descriptors, it cuts energy root-mean-square error by up to about one order of magnitude on CrN, Fe and FeGe. Spin-lattice simulations of Fe give a magnetic transition temperature near 1000 K, close to the experimental Curie temperature of 1043 K, and one consumer-grade GPU handles coupled dynamics of more than two million atoms.