Improved Grid Optimization and Fitting in Least Squares Tensor Hypercontraction

TL;DR

This paper introduces a Cholesky-based method for generating fitting grids in least-squares tensor hypercontraction, improving control, stability, and customization for quantum chemical calculations.

Contribution

It presents a novel Cholesky decomposition approach for grid generation in LS-THC, enhancing stability and allowing tailored grids for different charge distributions.

Findings

Grid quality is controlled by cutoff parameters and initial grid size.

The Cholesky method provides a stable, unique grid generation process.

The approach is effective for LS-DF-THC-MP2 calculations on various molecules.

Abstract

A new method for generating fitting grids for least-squares tensor hypercontraction (LS-THC) is presented. This method draws inspiration from the related interpolative separable density fitting (ISDF) technique, but uses only a pivoted Cholesky decomposition of the metric matrix, S, already computed as a matter of course in LS-THC. The size and quality of the resulting grid is controlled by a user-defined cutoff parameter and the size of the starting grid. Additionally, the Cholesky-based method provides an alternative and possible more numerically stable method for performing the least-squares fit. The quality of the grids produced is evaluated for LS-DF-THC-MP2 calculations on retinal and benzene, the former with a large starting grid and small cc-pVDZ basis set, and the latter with a wide range of grids and basis sets. The error and grid size is found to be well-controlled by either the cutoff parameter (with a large starting grid) or the starting grid size (with a tight cutoff) and highly predictable. The Cholesky-based method is also able to generate unique grids tailored to different charge distributions, for example the (ab|, (ai|, and (ij| distributions that arise in the molecular orbital integrals. While only the (ai| grid directly affects the MP2 energy, the relative sizes of the other grids are examined.