Efficiently Trained Deep Learning Potential for Graphane

We
have developed an accurate and efficient deep-learning potential
(DP) for graphane, which is a fully hydrogenated version of graphene,
using a very small training set consisting of 1000 snapshots from
a 0.5 ps density functional theory (DFT) molecular dynamics simulation
at 1000 K. We have assessed the ability of the DP to extrapolate to
system sizes, temperatures, and lattice strains not included in the
training set. The DP performs surprisingly well, outperforming an
empirical many-body potential when compared with DFT data for the
phonon density of states, thermodynamic properties, velocity autocorrelation
function, and stress–strain curve up to the yield point. This
indicates that our DP can reliably extrapolate beyond the limit of
the training data. We have computed the thermal fluctuations as a
function of system size for graphane. We found that graphane has larger
thermal fluctuations compared with graphene, but having about the
same out-of-plane stiffness.