  We present the development of a quadratic Spectral Neighbor Analysis
Potential (q-SNAP) for ferromagnetic cobalt and its applications to bulk
phases, surfaces, and nanoparticles. Trained on Density Functional Theory
calculations using the Perdew-Burke-Ernzerhof (DFT-PBE) functional, this
machine-learned potential enables simulations of large systems over extended
time scales across a wide range of temperatures and pressures at near DFT
accuracy. It is validated by closely reproducing the phonon dispersions of
hexagonal close-packed (hcp) and face-centered cubic (fcc) Co, surface
energies, and the relative stability of nanoparticles of various shapes.
Thermal expansion and the melting point of Co computed with this potential are
close to experimental values. Furthermore, this machine-learned potential goes
beyond the capabilities of simpler N-body potentials by capturing nuanced
properties such as vacancy formation energies on nanoparticle vertices. This
accuracy and versatility make the potential suitable for a wide range of
applications, including catalysis.
