An accurate and transferable machine learning interatomic potential for nickel

Nickel (Ni) is a magnetic transition metal with two allotropic phases, stable face-centered cubic (FCC) and metastable hexagonal close-packed (HCP), widely used in structural applications. Magnetism affects many mechanical and defect properties, but spin-polarized density functional theory (DFT) calculations are computationally inefficient for studying material behavior requiring large system sizes and/or long simulation times. Here we develop a “magnetism-hidden” machine-learning Deep Potential (DP) model for Ni without a descriptor for magnetic moments, using training datasets derived from spin-polarized DFT calculations. The DP-Ni model exhibits excellent transferability and representability for a wide-range of FCC and HCP properties, including (finite-temperature) lattice parameters, elastic constants, phonon spectra, and many defects. As an example of its applicability, we investigate the Ni FCC-HCP allotropic phase transition under (high-stress) uniaxial tensile loading. The high accurate DP model for magnetic Ni facilitates accurate large-scale atomistic simulations for complex phase transformation behavior and may serve as a foundation for developing interatomic potentials for Ni-based superalloys and other multi-principal component alloys. Ni can exist in both FCC and HCP phases, and is a key metal for structural applications. Here, a deep potential for atomistic simulations of nickel is derived from spin-polarized DFT calculations, and found to accurately predict the properties of both FCC and HCP phases.