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

Machine learning for molecular dynamics with strongly correlated electrons