We implemented our previously developed QRNN architecture to train the Li x C 6 model. Modified Behler and Parrinello symmetry functions were used to compute an atomic environment vector (AEV) for each atom. The AEV describes both local geometric and charge environment, and is composed of radial and angular symmetry functions for atom i (G AEV i ), charge-weighted radial AEV (G qR i ), and atomic charge (q i ). The AEV is then used as an input into an atomic neural network that transforms the atomic AEV into an output E i . The total energy E T of the system is the sum over all i "atomic contribution", and is defined as: