Multireference configuration interaction and perturbation theory without reduced density matrices

TL;DR

This paper introduces a stochastic approach to multireference configuration interaction and perturbation theory that eliminates the need for computationally expensive high-rank reduced density matrices, enabling more efficient calculations for large active spaces.

Contribution

The authors develop a flexible stochastic algorithm for MRCI and MRPT that avoids high-rank RDMs, applicable to various reference wave functions, and demonstrate its competitiveness and utility.

Findings

Stochastic algorithm reduces memory and computational costs.

Method is competitive with deterministic approaches for small active spaces.

Benchmark applications validate the approach's effectiveness.

Abstract

The computationally expensive evaluation and storage of high-rank reduced density matrices (RDMs) has been the bottleneck in the calculation of dynamic correlation for multireference wave functions in large active spaces. We present a stochastic formulation of multireference configuration interaction (MRCI) and perturbation theory (MRPT) that avoids the need for these expensive RDMs. The algorithm presented here is flexible enough to incorporate a wide variety of active space reference wave functions, including selected configuration interaction, matrix product states, and symmetry-projected Jastrow mean field wave functions. It enjoys the usual attractive features of Monte Carlo methods, such as embarrassing parallelizability and low memory costs. We find that the stochastic algorithm is already competitive with the deterministic algorithm for small active spaces, containing as few as 14 orbitals. We illustrate the utility of our stochastic formulation using benchmark applications.