https://mdmpathway.com/probabl....e-associated-variati
This work studies the capacity of transfer understanding, in particular discriminative fine-tuning, for effectively producing chemically accurate interatomic neural network potentials on organic particles from the MD17 and ANI data sets. We show that pre-training the network parameters on data acquired from density functional computations considerably improves the test efficiency of designs trained on more accurate abdominal initio information. Also, we show that fine-tuning with power
Like
Comment
Share
