  Machine learning methods for solving the equations of dynamical mean-field
theory are developed. The method is demonstrated on the three dimensional
Hubbard model. The key technical issues are defining a mapping of an input
function to an output function, and distinguishing metallic from insulating
solutions. Both metallic and Mott insulator solutions can be predicted. The
validity of the machine learning scheme is assessed by comparing predictions of
full correlation functions, of quasi-particle weight and particle density to
values directly computed. The results indicate that with modest further
development, machine learning approach may be an attractive computational
efficient option for real materials predictions for strongly correlated
systems.
