Go Green: Selected Configuration Interaction as a More Sustainable Alternative for High Accuracy

TL;DR

This paper demonstrates that selected configuration interaction (SCI) methods can achieve high-accuracy results comparable to full configuration interaction (FCI) with significantly less computational resources, promoting sustainability in computational chemistry.

Contribution

It shows how SCI, specifically the CIPSI algorithm, can efficiently approximate FCI energies, reducing computational costs and environmental impact.

Findings

SCI achieves microhartree accuracy with fewer determinants.

SCI reduces computational and memory costs compared to FCI.

SCI offers a sustainable alternative for high-accuracy quantum chemistry calculations.

Abstract

Recently, a new distributed implementation of the full configuration interaction (FCI) method has been reported [Gao et al. J. Chem Theory Comput. 2024, 20, 1185]. Thanks to a hybrid parallelization scheme, the authors were able to compute the exact energy of propane (\ce{C3H8}) in the minimal basis STO-3G. This formidable task involves handling an active space of 26 electrons in 23 orbitals or a Hilbert space of \SI{1.3d12} determinants. This is, by far, the largest FCI calculation reported to date. Here, we illustrate how, from a general point of view, selected configuration interaction (SCI) can achieve microhartree accuracy at a fraction of the computational and memory cost, via a sparse exploration of the FCI space. The present SCI calculations are performed with the \textit{Configuration Interaction using a Perturbative Selection made Iteratively} (CIPSI) algorithm, as implemented in a determinant-driven way in the \textsc{quantum package} software. The present study reinforces the common wisdom that among the exponentially large number of determinants in the FCI space, only a tiny fraction of them significantly contribute to the energy. More importantly, it demonstrates the feasibility of achieving comparable accuracy using more reasonable and sustainable computational resources, hence reducing the ever-growing carbon footprint of computational chemistry.