arXiv:2601.07742v4 Announce Type: replace-cross Abstract: Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIPs) trained with 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,
The continuous drive for more accurate and efficient materials modeling in AI and scientific computing leads to refined techniques like phonon fine-tuning.
Improved accuracy in machine-learned interatomic potentials enhances the reliability of materials design and discovery, accelerating research in critical areas.
Machine learning models for material properties can now achieve higher fidelity in predicting vibrational characteristics, crucial for many applications.
- · Materials scientists
- · AI/ML researchers in chemistry
- · Semiconductor industry
- · Drug discovery
- · Traditional QM/MD methods (in some applications)
- · Companies with less sophisticated MLIPs
More accurate predictions of material properties lead to faster and more cost-effective materials discovery.
Accelerated development of novel materials for energy, electronics, and other advanced manufacturing sectors.
Enhanced scientific understanding of material behaviors at an atomic level, potentially leading to breakthroughs in fields like quantum computing or sustainable energy.
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Read at arXiv cs.LG