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 ($N\ensuremath{\gtrsim}{10}^{3}$). We investigate the physics of the smooth Mott crossover in the fluid phase.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 ($N\ensuremath{\gtrsim}{10}^{3}$). We investigate the physics of the smooth Mott crossover in the fluid phase.

arXiv:1811.01914Physical review. B./Physical review. BApr 8, 201940 citations

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 ($N\ensuremath{\gtrsim}{10}^{3}$). We investigate the physics of the smooth Mott crossover in the fluid phase.