Fine-tuning universal MACE potentials on targeted datasets generally improves accuracy and convergence speed, though data selection, not the foundation model alone, determines success.
Surrogate models for vibrational entropy based on a spatial decomposition
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abstract
The temperature-dependent behavior of defect densities within a crystalline structure is intricately linked to the phenomenon of vibrational entropy. Traditional methods for evaluating vibrational entropy are computationally intensive, limiting their practical utility. We show that total entropy can be decomposed into atomic site contributions and rigorously estimate the locality of site entropy. This analysis suggests that vibrational entropy can be effectively predicted using a surrogate model for site entropy. We employ machine learning to develop such a surrogate models employing the Atomic Cluster Expansion model. We supplement our rigorous analysis with an empirical convergence study. In addition we demonstrate the performance of our method for predicting vibrational formation entropy and attempt frequency of the transition rates, on point defects such as vacancies and interstitials.
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A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs)
Fine-tuning universal MACE potentials on targeted datasets generally improves accuracy and convergence speed, though data selection, not the foundation model alone, determines success.