  Developing machine learning-based interatomic potentials from ab-initio
electronic structure methods remains a challenging task for computational
chemistry and materials science. 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. We show that pre-training the network
parameters on data obtained from density functional calculations considerably
improves the sample efficiency of models trained on more accurate ab-initio
data. Additionally, we show that fine-tuning with energy labels alone can
suffice to obtain accurate atomic forces and run large-scale atomistic
simulations, provided a well-designed fine-tuning data set. We also investigate
possible limitations of transfer learning, especially regarding the design and
size of the pre-training and fine-tuning data sets. Finally, we provide GM-NN
potentials pre-trained and fine-tuned on the ANI-1x and ANI-1ccx data sets,
which can easily be fine-tuned on and applied to organic molecules.
