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Keyword: universal interatomic potentials ×Clear all

2 claims from 1 paper

1. Materials Science › Machine Learning in Materials Science

   Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models

   Hänseroth, Flötotto and Dreßler · The Journal of Physical Chemistry Letters · 2026

   The paper compares five universal machine-learning potentials on seven systems and proposes using them to generate configurations, relabelled with DFT, to train accurate material-specific models.

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   1. UncheckedAcross the tested systems, about 2,000 structures recalculated with DFT are often enough to give accurate fine-tuned or from-scratch machine-learning potentials.“Across the tested systems, $2{,}000$ DFT-recalculated structures are often sufficient to obtain accurate fine-tuned or trained-from-scratch models.”
   2. UncheckedThe authors' workflow can produce a 1 ns ab initio-quality simulation trajectory, including training data and model creation, within three days.“The resulting workflow enables the generation of $1$ ns ab initio-quality trajectories - including training data generation and model creation - within three days.”