dorsal/arxiv
View SchemaPFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials
| Authors | Teddy Koker, Abhijeet Gangan, Mit Kotak, Jaime Marian, Tess Smidt |
|---|---|
| Categories | |
| ArXiv ID | 2601.07742vv3 |
| URL | https://arxiv.org/abs/2601.07742 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIPs) trained with standard a standard loss on energy, force, and stress errors can exhibit error in curvature, degrading the prediction of vibrational properties. We introduce phonon fine-tuning (PFT), which directly supervises second-order force constants of materials by matching MLIP energy Hessians to DFT-computed force constants from finite displacement phonon calculations. To scale to large supercells, PFT stochastically samples Hessian columns and computes the loss with a single Hessian-vector product. We also use a simple co-training scheme to incorporate upstream data to mitigate catastrophic forgetting. On the MDR Phonon benchmark, PFT improves Nequix MP (trained on Materials Project) by 55% on average across phonon thermodynamic properties and achieves state-of-the-art performance among models trained on Materials Project trajectories. PFT also generalizes to improve properties beyond second-derivatives, improving thermal conductivity predictions that rely on third-order derivatives of the potential energy.
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"abstract": "Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIPs) trained with standard a standard loss on energy, force, and stress errors can exhibit error in curvature, degrading the prediction of vibrational properties. We introduce phonon fine-tuning (PFT), which directly supervises second-order force constants of materials by matching MLIP energy Hessians to DFT-computed force constants from finite displacement phonon calculations. To scale to large supercells, PFT stochastically samples Hessian columns and computes the loss with a single Hessian-vector product. We also use a simple co-training scheme to incorporate upstream data to mitigate catastrophic forgetting. On the MDR Phonon benchmark, PFT improves Nequix MP (trained on Materials Project) by 55% on average across phonon thermodynamic properties and achieves state-of-the-art performance among models trained on Materials Project trajectories. PFT also generalizes to improve properties beyond second-derivatives, improving thermal conductivity predictions that rely on third-order derivatives of the potential energy.",
"arxiv_id": "2601.07742",
"authors": [
"Teddy Koker",
"Abhijeet Gangan",
"Mit Kotak",
"Jaime Marian",
"Tess Smidt"
],
"categories": [
"cond-mat.mtrl-sci",
"cs.LG"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials",
"url": "https://arxiv.org/abs/2601.07742",
"version": "v3"
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