Softmax parameterization of the occupation numbers for natural orbital functionals based on electron pairing approaches

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Autori principali:	Franco, L., Bonfil-Rivera, I. A., Lew-Yee, J. F. Huan, Piris, M., del Campo, J. M., Vargas-Hernández, R. A.
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Chemical Physics
Accesso online:	Acceder al recurso
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author	Franco, L. Bonfil-Rivera, I. A. Lew-Yee, J. F. Huan Piris, M. del Campo, J. M. Vargas-Hernández, R. A.
author_facet	Franco, L. Bonfil-Rivera, I. A. Lew-Yee, J. F. Huan Piris, M. del Campo, J. M. Vargas-Hernández, R. A.
contents	Within the framework of natural orbital functional theory, having a convenient representation of the occupation numbers and orbitals becomes critical for the computational performance of the calculations. Recognizing this, we propose an innovative parametrization of the occupation numbers that takes advantage of the electron-pairing approach used in Piris natural orbital functionals through the adoption of the softmax function, a pivotal component in modern deep-learning models. Our approach not only ensures adherence to the N-representability of the first-order reduced density matrix (1RDM) but also significantly enhances the computational efficiency of 1RDM functional theory calculations. The effectiveness of this alternative parameterization approach was assessed using the W4-17-MR molecular set, which demonstrated faster and more robust convergence compared to previous implementations.
format	Preprint
id	arxiv_https___arxiv_org_abs_2403_09463
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Softmax parameterization of the occupation numbers for natural orbital functionals based on electron pairing approaches Franco, L. Bonfil-Rivera, I. A. Lew-Yee, J. F. Huan Piris, M. del Campo, J. M. Vargas-Hernández, R. A. Chemical Physics Within the framework of natural orbital functional theory, having a convenient representation of the occupation numbers and orbitals becomes critical for the computational performance of the calculations. Recognizing this, we propose an innovative parametrization of the occupation numbers that takes advantage of the electron-pairing approach used in Piris natural orbital functionals through the adoption of the softmax function, a pivotal component in modern deep-learning models. Our approach not only ensures adherence to the N-representability of the first-order reduced density matrix (1RDM) but also significantly enhances the computational efficiency of 1RDM functional theory calculations. The effectiveness of this alternative parameterization approach was assessed using the W4-17-MR molecular set, which demonstrated faster and more robust convergence compared to previous implementations.
title	Softmax parameterization of the occupation numbers for natural orbital functionals based on electron pairing approaches
topic	Chemical Physics
url	https://arxiv.org/abs/2403.09463

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