Fine-tuning ML interatomic potentials via a new LoRA-based Equitrain framework with minimal additional data improves phonon and thermal predictions over base and scratch-trained models in 53 systems.
However, the ML model captures an anharmonic potential and relaxes to the same primitive unit cell with same space group, differing only by slightly adjusted lattice parameters
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Parameter-Efficient Fine-Tuning of Machine-Learning Interatomic Potentials for Phonon and Thermal Properties
Fine-tuning ML interatomic potentials via a new LoRA-based Equitrain framework with minimal additional data improves phonon and thermal predictions over base and scratch-trained models in 53 systems.