  We use machine learning to enable large-scale molecular dynamics (MD) of a
correlated electron model under the Gutzwiller approximation scheme. This model
exhibits a Mott transition as a function of on-site Coulomb repulsion $U$. The
repeated solution of the Gutzwiller self-consistency equations would be
prohibitively expensive for large-scale MD simulations. We show that machine
learning models of the Gutzwiller potential energy can be remarkably accurate.
The models, which are trained with $N=33$ atoms, enable highly accurate MD
simulations at much larger scales ($N\gtrsim10^{3}$). We investigate the
physics of the smooth Mott crossover in the fluid phase.
