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

TL;DR

This paper introduces a machine learned interatomic potential for Fe7Cr2Ni alloy that incorporates spin effects, achieving near-DFT accuracy while enabling larger-scale simulations of steel aging processes.

Contribution

The authors developed a Spin GAP model that extends existing potentials to include collinear spin effects, improving the prediction of magnetic and structural properties in steel alloys.

Findings

Accurately models alloy properties with DFT-level precision.

Successfully predicts structural distortions from magnetic states.

Validates vacancy and bulk properties against DFT data.

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.