  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 "magnetism-hidden" DP-Ni model exhibits high 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
DP model for magnetic Ni facilitates accurate large-scale atomistic simulations
for complex mechanical behavior and serves as a foundation for developing
interatomic potentials for Ni-based superalloys and other multi-principal
component alloys.
