A Comprehensive Assessment and Benchmark Studyof Large Atomistic Foundation Models for Phonons

Author(s)

Md Zaibul Anam
Ogheneyoma Aghoghovbia
Mohammed Al-Fahdi
Lingyu Kong
Victor Fung
Ming Hu, University of South CarolinaFollow

Document Type

Article

Abstract

The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces. While their capability for modeling phonon properties is emerging, systematic benchmarking across chemically diverse systems remains limited. We evaluate six recent uMLPs—EquiformerV2, MatterSim, MACE, and CHGNet—on 2429 crystalline materials from the Open Quantum Materials Database. Models were used to compute atomic forces in displaced supercells, derive interatomic force constants (IFCs), and predict phonon properties including lattice thermal conductivity (LTC), compared with density functional theory and experimental data. The EquiformerV2 pretrained model trained on the OMat24 dataset exhibits strong performance in predicting atomic forces and third-order IFCs, while its fine-tuned counterpart consistently outperforms other models in predicting second-order IFCs, LTC, and other phonon properties. Although MACE and CHGNet demonstrated comparable force prediction accuracy to EquiformerV2, notable discrepancies in IFC fitting led to poor LTC predictions. Conversely, MatterSim, despite lower force accuracy, achieved intermediate IFC predictions, suggesting error cancellation and complex relationships between force accuracy and phonon predictions. This benchmark guides the evaluation and selection of uMLPs for high-throughput screening of materials with targeted thermal transport properties.

Digital Object Identifier (DOI)

https://doi.org/10.1002/aidi.202500075

Publication Info

Published in Advanced Intelligent Discovery, 2025.

Rights

© 2025 The Author(s). Advanced Intelligent Discovery published by Wiley-VCH GmbH

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

APA Citation

Anam, M. Z., Aghoghovbia, O., Al‐Fahdi, M., Kong, L., Fung, V., & Hu, M. (2025). A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons. Advanced Intelligent Discovery.https://doi.org/10.1002/aidi.202500075