A collinear-spin machine learned interatomic potential for Fe$_{7}$Cr$_{2}$Ni alloy

Abstract: 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.