Incremental Embedding: A Density Matrix Embedding Scheme for Molecules

TL;DR

Incremental Embedding introduces a flexible, overlapping fragment-based density matrix embedding scheme that converges quickly and reduces computational cost while accurately capturing static correlation in molecules.

Contribution

It proposes a novel Schmidt decomposition-based embedding method allowing overlapping fragments without predefined edge sites, improving accuracy and efficiency.

Findings

Converges rapidly with increasing fragment size.

Recovers most static correlation in small basis sets.

Lower computational scaling than many wave function methods.

Abstract

The idea of using fragment embedding to circumvent the high computational scaling of accurate electronic structure methods while retaining high accuracy has been a long-standing goal for quantum chemists. Traditional fragment embedding methods mainly focus on systems composed of weakly correlated parts and are insufficient when division across chemical bonds is unavoidable. Recently, density matrix embedding theory (DMET) and other methods based on the Schmidt decomposition have emerged as a fresh approach to this problem. Despite their success on model systems, these methods can prove difficult for realistic systems because they rely on either a rigid, non-overlapping partition of the system or a specification of some special sites (i.e. `edge' and `center' sites), neither of which is well-defined in general for real molecules. In this work, we present a new Schmidt decomposition-based embedding scheme called Incremental Embedding that allows the combination of arbitrary overlapping fragments without the knowledge of edge sites. This method forms a convergent hierarchy in the sense that higher accuracy can be obtained by using fragments involving more sites. The computational scaling for the first few levels is lower than that of most correlated wave function methods. We present results for several small molecules in atom-centered Gaussian basis sets and demonstrate that Incremental Embedding converges quickly with fragment size and recovers most static correlation in small basis sets even when truncated at the second lowest level.