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We have developed a new machine learned interatomic potential for theprototypical austenitic steel Fe$_{7}$Cr$_{2}$Ni, using the Gaussianapproximation potential (GAP) framework. This new GAP can model the alloy'sproperties with higher accuracy than classical interatomic potentials likeembedded atom models (EAM), while also allowing us to collect much morestatistics than expensive first-principles methods like density functionaltheory (DFT). We also extended the GAP input descriptors to approximate theeffects of collinear spins (Spin GAP), and demonstrate how this extended modelsuccessfully predicts low temperature structural distortions due to theantiferromagnetic spin state. We demonstrate the application of the Spin GAPmodel for bulk properties and vacancies and validate against DFT. These resultsare a step towards modelling ageing in austenitic steels with close to DFTaccuracy but at a fraction of its cost.

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