Next Generation Dynamical Mean-Field Theory Calculations: the ASCI-DMFT Method

The study of strongly correlated electronic systems is one of the main open challenges in condensed matter physics. The dynamical mean-field theory (DMFT) method, which self-consistently maps an intractable strongly correlated lattice problem into a numerically solvable impurity Anderson model, has been very successful in describing the quantum phases and spectra of phenomenological and ab initio models. However, the range of physics that can be studied with DMFT is naturally limited by the complexity of impurity models that the algorithm can handle. For zero temperature studies, configuration interaction (CI) methods have been introduced into DMFT calculations to efficiently compute the ground state of the impurity Hamiltonian. Here we employ the recently proposed adaptive sampling CI (ASCI) algorithm, building on its key ability to identify the most relevant states to describe the ground state in a given basis, to solve the impurity model and thereby construct an extremely efficient ASCI-DMFT algorithm. We provide evidence that this novel implementation will be able to perform with higher efficiency and better scaling than previous zero temperature DMFT impurity solvers, opening the door to a new generation of studies of strongly correlated physics.