  Phonons play a critical role in determining various material properties, but
conventional methods for phonon calculations are computationally intensive,
limiting their broad applicability. In this study, we present an approach to
accelerate high-throughput harmonic phonon calculations using machine learning
universal potentials. We train a state-of-the-art machine learning interatomic
potential, based on multi-atomic cluster expansion (MACE), on a comprehensive
dataset of 2,738 crystal structures with 77 elements, totaling 15,670 supercell
structures, computed using high-fidelity density functional theory (DFT)
calculations. Our approach significantly reduces the number of required
supercells for phonon calculations while maintaining high accuracy in
predicting harmonic phonon properties across diverse materials. The trained
model is validated against phonon calculations for a held-out subset of 384
materials, achieving a mean absolute error (MAE) of 0.18 THz for vibrational
frequencies from full phonon dispersions, 2.19 meV/atom for Helmholtz
vibrational free energies at 300K, as well as a classification accuracy of
86.2% for dynamical stability of materials. A thermodynamic analysis of
polymorphic stability in 126 systems demonstrates good agreement with DFT
results at 300 K and 1000 K. In addition, the diverse and extensive
high-quality DFT dataset curated in this study serves as a valuable resource
for researchers to train and improve other machine learning interatomic
potential models.
