| Issue |
ESAIM: ProcS
Volume 73, 2023
CEMRACS 2021 - Data Assimilation and Reduced Modeling for High Dimensional Problems
|
|
|---|---|---|
| Page(s) | 107 - 129 | |
| DOI | https://doi.org/10.1051/proc/202373107 | |
| Published online | 30 August 2023 | |
On basis set optimisation in quantum chemistry
1
CERMICS, Ecole des Ponts and Inria Paris, 6 & 8 avenue Blaise Pascal, 77455 Marne-la-Vallée, France
2
Laboratoire de Mathématiques de Besançon, UMR CNRS 6623, Université Bourgogne Franche-Comté, 16 route de Gray, 25030 Besançon, France
3
CERMICS, Ecole des Ponts and Inria Paris, 6 & 8 avenue Blaise Pascal, 77455 Marne-la-Vallée, France
4
CERMICS, Ecole des Ponts and Inria Paris, 6 & 8 avenue Blaise Pascal, 77455 Marne-la-Vallée, France
In this article, we propose general criteria to construct optimal atomic centered basis sets in quantum chemistry. We focus in particular on two criteria, one based on the ground-state one-body density matrix of the system and the other based on the ground-state energy. The performance of these two criteria are then numerically tested and compared on a parametrized eigenvalue problem, which corresponds to a one-dimensional toy version of the ground-state dissociation of a diatomic molecule.
Résumé
Nous proposons dans cet article des critères généraux pour construire des bases atomiques localisées optimales en chimie quantique. Nous nous intéressons en particulier à deux critères: l’un basé sur la matrice densité à un corps du système, l’autre basé sur l’énergie de l’état fondamental du système. Les performances de ces deux critères sont évaluées et comparées sur un problème aux valeurs propres paramétré, correspondant à une version simplifiée du problème de dissociation de l’état fondamental d’une molécule diatomique.
e-mail: eric.cances@enpc.fr
e-mail: genevieve.dusson@math.cnrs.fr
e-mail: gaspard.kemlin@enpc.fr
e-mail: laurent.vidal@enpc.fr
© EDP Sciences, SMAI 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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