  A machine-learned spin-lattice interatomic potential (MSLP) for magnetic iron
is developed and applied to mesoscopic scale defects. It is achieved by
augmenting a spin-lattice Hamiltonian with a neural network term trained to
descriptors representing a mix of local atomic configuration and magnetic
environments. It reproduces the cohesive energy of BCC and FCC phases with
various magnetic states. It predicts the formation energy and complex magnetic
structure of point defects in quantitative agreement with density functional
theory (DFT) including the reversal and quenching of magnetic moments near the
core of defects. The Curie temperature is calculated through spin-lattice
dynamics showing good computational stability at high temperature. The
potential is applied to study magnetic fluctuations near sizable dislocation
loops. The MSLP transcends current treatments using DFT and molecular dynamics,
and surpasses other spin-lattice potentials that only treat near-perfect
crystal cases.

Materials Science;Disordered Systems and Neural Networks;Atomic Physics;Computational Physics