Automatic generation of complementary auxiliary basis sets (CABS) for explicitly correlated methods

TL;DR

This paper introduces autoCABS, a simple algorithm for automatically generating complementary auxiliary basis sets (CABS) for explicitly correlated methods, demonstrating comparable quality to optimized sets across various basis sizes.

Contribution

The paper presents autoCABS, a new algorithm and open-source implementation for auto-generating CABS, filling a gap in existing auxiliary basis set generation methods.

Findings

autoCABS-generated CABS are comparable to purpose-optimized sets

Quality difference diminishes with larger basis sets

autoCABS simplifies auxiliary basis set generation process

Abstract

Explicitly correlated calculations, aside from the orbital basis set, typically require three auxiliary basis sets: JK (Coulomb-exchange fitting), RI-MP2 (resolution of the identity MP2), and CABS (complementary auxiliary basis set). If unavailable for the orbital basis set and chemical elements of interest, the first two can be auto-generated on the fly using existing algorithms, but not the third. In this paper, we present a quite simple algorithm named autoCABS; a Python implementation under a free software license is offered at Github. For the cc-pVnZ-F12 (n=D,T,Q,5) and the W4-08 thermochemical benchmark, we demonstrate that autoCABS-generated CABS basis sets are comparable in quality to purpose-optimized OptRI basis sets from the literature, and that the quality difference becomes entirely negligible as n increases.