Machine learning (ML) is emerging as a promising tool to help model various types of many-body phenomena. A promising research area is the molecular dynamics (MD) of strongly correlated electron materials. While quantum MD methods based on density functional theory (DFT) have been successfully applied to a wide variety of materials, they have limited validity in their treatment of electron correlations. On the other hand, most of the many-body techniques, such as the dynamical mean-field theory, are computationally too costly for MD simulations. I will show that ML can be effective for building fast, linear-scaling MD potentials that capture correlated electron physics. Specifically, we use ML to enable large-scale Gutzwiller MD simulations of a liquid Hubbard model and study the Mott metal-insulator transition. For the systems considered in the present study, ML is up to 6 orders of magnitude faster than direct quantum calculations. The present work opens a path toward large-scale dynamical simulation of realistic models of correlated materials.
Contact: Cristian Batista, email@example.com