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arXiv Machine Learning 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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