  Recently, machine learning (ML) has been used to address the computational
cost that has been limiting ab initio molecular dynamics (AIMD). Here, we
present GNNFF, a graph neural network framework to directly predict atomic
forces from automatically extracted features of the local atomic environment
that are translationally-invariant, but rotationally-covariant to the
coordinate of the atoms. We demonstrate that GNNFF not only achieves high
performance in terms of force prediction accuracy and computational speed on
various materials systems, but also accurately predicts the forces of a large
MD system after being trained on forces obtained from a smaller system.
Finally, we use our framework to perform an MD simulation of Li7P3S11, a
superionic conductor, and show that resulting Li diffusion coefficient is
within 14% of that obtained directly from AIMD. The high performance exhibited
by GNNFF can be easily generalized to study atomistic level dynamics of other
material systems.
