  A data-driven framework is presented for building magneto-elastic
machine-learning interatomic potentials (ML-IAPs) for large-scale spin-lattice
dynamics simulations. The magneto-elastic ML-IAPs are constructed by coupling a
collective atomic spin model with an ML-IAP. Together they represent a
potential energy surface from which the mechanical forces on the atoms and the
precession dynamics of the atomic spins are computed. Both the atomic spin
model and the ML-IAP are parametrized on data from first-principles
calculations. We demonstrate the efficacy of our data-driven framework across
magneto-structural phase transitions by generating a magneto-elastic ML-IAP for
{\alpha}-iron. The combined potential energy surface yields excellent agreement
with first-principles magneto-elastic calculations and quantitative predictions
of diverse materials properties including bulk modulus, magnetization, and
specific heat across the ferromagnetic-paramagnetic phase transition.
