Machine learning for many-body physics: efficient solution of dynamical mean-field theory
This work addresses computational efficiency challenges in predicting real materials for strongly correlated systems, but it appears incremental as it builds on existing methods with modest further development needed.
The authors tackled the problem of solving dynamical mean-field theory equations for many-body physics by developing a machine learning method, demonstrating it on the three-dimensional Hubbard model to predict metallic and Mott insulator solutions with results validated against direct computations.
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.