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Abstract Machine-learning interatomic potentials are widely used as computationally efficient surrogates for density functional theory in atomistic simulations, enabling large-scale and long-time modeling of materials systems. Here, we investigate how different fine-tuning strategies affect the prediction of harmonic phonon band structures, thermal and elastic properties, and the potential-energy surface along unstable phonon modes.
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