Sponsor
H. L. and Y. X. acknowledge support from the US National Science Foundation through award DMR-2317008. C. W. acknowledges support from the US National Science Foundation through the Office of Advanced Cyberstructure under award OAC-2311203. We acknowledge the computing resources provided by Bridges2 at Pittsburgh Supercomputing Center (PSC) through allocations mat220006p and mat220008p from the Advanced Cyber-infrastructure Coordination Ecosystem: Services & Support (ACCESS) program.
Published In
Materials Today Physics
Document Type
Pre-Print
Publication Date
4-1-2025
Subjects
Phonons -- machine learning
Abstract
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 (MLIPs) combined with an efficient training dataset generation strategy. Instead of computing phonon properties from a large number of supercells with small atomic displacements of a single atom, our approach uses a smaller subset of supercell structures where all atoms are randomly displaced by 0.01 to 0.05 UŮ, significantly reducing computational costs. We train a state-of-the-art MLIP based on multi-atomic cluster expansion (MACE), on a comprehensive dataset of 2738 materials with 77 elements, totaling 15,670 supercell structures, computed using high-fidelity density functional theory (DFT) calculations. 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.
Rights
© Copyright the author(s) 2025
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DOI
10.1016/j.mtphys.2025.101688
Citation Details
Published as: Lee, H., Hegde, V. I., Wolverton, C., & Xia, Y. (2025). Accelerating high-throughput phonon calculations via machine learning universal potentials. Materials Today Physics, 53, 101688.
Description
This is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published as: Accelerating high-throughput phonon calculations via machine learning universal potentials. Materials Today Physics, 101688.