Dynamical Mean-Field Theory Simulations with the Adaptive Sampling Configuration Interaction Method

TL;DR

This paper introduces an efficient impurity solver based on the adaptive sampling configuration interaction (ASCI) method for dynamical mean-field theory (DMFT), enabling faster and more scalable simulations of strongly correlated quantum systems.

Contribution

The authors adapt the ASCI method as an impurity solver for DMFT, significantly improving computational efficiency and scalability for complex many-body systems.

Findings

ASCI-DMFT is several orders of magnitude faster than previous methods.

The approach allows simulation of larger impurity clusters beyond current capabilities.

Efficient convergence demonstrated on Hubbard models.

Abstract

In the pursuit of accurate descriptions of strongly correlated quantum many-body systems, dynamical mean-field theory (DMFT) has been an invaluable tool for elucidating the spectral properties and quantum phases of both phenomenological models and ab initio descriptions of real materials. Key to the DMFT process is the self-consistent map of the original system into an Anderson impurity model, the ground state of which is computed using an impurity solver. The power of the method is thus limited by the complexity of the impurity model the solver can handle. Simulating realistic systems generally requires many correlated sites. By adapting the recently proposed adaptive sampling configuration interaction (ASCI) method as an impurity solver, we enable much more efficient zero temperature DMFT simulations. The key feature of the ASCI method is that it selects only the most relevant Hilbert space degrees of freedom to describe the ground state. This reduces the numerical complexity of the calculation, which will allow us to pursue future DMFT simulations with more correlated impurity sites than in previous works. Here we present the ASCI-DMFT method and example calculations on the one-dimensional and two-dimensional Hubbard models that exemplify its efficient convergence and timing properties. We show that the ASCI approach is several orders of magnitude faster than the current best published ground state DMFT simulations, which allows us to study the bath discretization error in simulations with small clusters, as well as to address cluster sizes beyond the current state of the art. Our approach can also be adapted for other embedding methods such as density matrix embedding theory and self-energy embedding theory.