  Machine learned chemical potentials have shown great promise as alternatives
to conventional computational chemistry methods to represent the potential
energy of a given atomic or molecular system as a function of its geometry.
However, such potentials are only as good as the data they are trained on, and
building a comprehensive training set can be a costly process. Therefore, it is
important to extract as much information from training data as possible without
further increasing the computational cost. One way to accomplish this is by
training on molecular forces in addition to energies. This allows for three
additional labels per atom within the molecule. Here we develop a neural
network potential energy surface for studying a hydrogen transfer reaction
between two conformers of C5H5. We show that, for a much smaller training set,
force training can greatly improve the accuracy of the model compared to only
training to energies. We also demonstrate the importance of choosing the proper
force to energy weight ratio for the loss function to minimize the model test
error.
