# Machine learning for molecular dynamics with strongly correlated electrons

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 the density functional theory 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. We showed that ML can be effective for building fast, linear-scaling MD potentials that capture correlated electron physics. Specifically, we used ML to enable large-scale Gutzwiller MD simulations of a liquid Hubbard model and studied 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. Our work opens a path toward a large-scale dynamical simulation of realistic models of correlated materials.

- Hidemaro Suwa, Justin S. Smith, Nicholas Lubbers, Cristian D. Batista, Gia-Wei Chern, and Kipton Barros
*Machine learning for molecular dynamics with strongly correlated electrons*

Phys. Rev. B**99**, 161107(R) (2019). (preprint: arXiv:1811.01914)