https://pikfyvesignals.com/ind....ex.php/the-consequen
This work studies the capacity of transfer learning, in particular discriminative fine-tuning, for effortlessly producing chemically accurate interatomic neural network potentials on organic molecules from the MD17 and ANI data units. We show that pre-training the network parameters on information obtained from density useful calculations considerably improves the sample efficiency of models trained on more accurate abdominal initio data. Additionally, we show that fine-tuning
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