Bio: Dominika Zgid is an assistant professor of Chemistry at the University of Michigan. She received her Ph.D. from the University of Waterloo, Canada, in 2008. Since starting at Michigan, she has received a DOE Early Career Award in 2013 and an NSF Career Award in 2015.

Her main interests are at the interface of theoretical chemistry and condensed matter physics with a major focus on designing new, systematically improvable and controlled computational methods that can be used to study strongly correlated molecules and materials. She has worked on a variety of topics, such as a molecular version of density matrix renormalization group, solvers for dynamical mean field theory using explicit bath formulation, conserving Green’s function methods for weakly correlated systems and the development of the self-energy embedding theory.

Towards Accurate Quantum-Mechanical Calculations beyond Density Functional Theory on Large Systems

We present a detailed discussion of self-energy embedding theory (SEET) which is a quantum embedding scheme allowing us to describe a chosen subsystem very accurately while keeping the description of the environment at a lower cost. We apply SEET to molecular examples where commonly our chosen subsystem is made out of a set of strongly correlated orbitals while the weakly correlated orbitals constitute an environment. Such a self-energy separation is very general and to make this procedure applicable to multiple systems a detailed and practical procedure for the evaluation of the system and environment self-energy is necessary. We list all the intricacies for one of the possible procedures while focusing our discussion on many practical implementation aspects such as the choice of best orbital basis, impurity solver, and many steps necessary to reach chemical accuracy.

Finally, on a set of carefully chosen molecular examples, we demonstrate that SEET, which is a controlled, systematically improvable Green’s function method can be as accurate as established wavefunction quantum chemistry methods.