  Molecular modeling is an important topic in drug discovery. Decades of
research have led to the development of high quality scalable molecular force
fields. In this paper, we show that neural networks can be used to train a
universal approximator for energy potential functions. By incorporating a fully
automated training process we have been able to train smooth, differentiable,
and predictive potential functions on large-scale crystal structures. A variety
of tests have also been performed to show the superiority and versatility of
the machine-learned model.
