Transfer learning for chemically accurate interatomic neural network potentials

1 code implementation • 7 Dec 2022 • Viktor Zaverkin, David Holzmüller, Luca Bonfirraro, Johannes Kästner

This work studies the capability of transfer learning, in particular discriminative fine-tuning, for efficiently generating chemically accurate interatomic neural network potentials on organic molecules from the MD17 and ANI data sets.

Atomic Forces Transfer Learning