Fast exchange with Gaussian basis set using robust pseudospectral method

TL;DR

This paper introduces a fast, memory-efficient algorithm for evaluating the exchange matrix in periodic systems with Gaussian basis sets, eliminating FFTs and reducing computational cost while maintaining high accuracy.

Contribution

The authors develop a novel algorithm combining a robust pseudospectral method, occ-RI exchange, and ISDF to significantly reduce the computational prefactor for exchange matrix evaluation.

Findings

The new algorithm retains cubic scaling with a significantly reduced prefactor.

It achieves high accuracy with a small number of auxiliary functions due to exponential error decay.

The method eliminates the need for FFTs during exchange matrix construction.

Abstract

In this article we present an algorithm to efficiently evaluate the exchange matrix in periodic systems when Gaussian basis set with pseudopotentials are used. The usual algorithm for evaluating exchange matrix scales cubically with the system size because one has to perform O(N2) fast Fourier transforms (FFT). Here we introduce an algorithm that retains the cubic scaling but reduces the prefactor significantly by eliminating the need to do FFTs during each exchange build. This is accomplished by representing the products of Gaussian basis function using a linear combination of an auxiliary basis the number of which scales linearly with the size of the system. We store the potential due to these auxiliary functions in memory which allows us to obtain the exchange matrix without the need to do FFT, albeit at the cost of additional memory requirement. Although the basic idea of using auxiliary functions is not new, our algorithm is cheaper due to a combination of three ingredients: (a) we use robust Pseudospectral method that allows us to use a relatively small number of auxiliary basis to obtain high accuracy (b) we use occ-RI exchange which eliminates the need to construct the full exchange matrix and (c) we use the (interpolative separable density fitting) ISDF algorithm to construct these auxiliary basis that are used in the robust pseudospectral method. The resulting algorithm is accurate and we note that the error in the final energy decreases exponentially rapidly with the number of auxiliary functions.