arXiv Machine Learning June 2, 2026 By Teddy Koker, Abhijeet Gangan, Mit Kotak, Jaime Marian, Tess Smidt

PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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