  We have developed a new machine learned interatomic potential for the
prototypical austenitic steel Fe$_{7}$Cr$_{2}$Ni, using the Gaussian
approximation potential (GAP) framework. This new GAP can model the alloy's
properties with close to density functional theory (DFT) accuracy, while at the
same time allowing us to access larger length and time scales than expensive
first-principles methods. We also extended the GAP input descriptors to
approximate the effects of collinear spins (Spin GAP), and demonstrate how this
extended model successfully predicts structural distortions due to
antiferromagnetic and paramagnetic spin states. We demonstrate the application
of the Spin GAP model for bulk properties and vacancies and validate against
DFT. These results are a step towards modelling the atomistic origins of ageing
in austenitic steels with higher accuracy.
