Data-driven approach for benchmarking DFTB-approximate excited state methods-Reference-Cited by-同舟云学术

Data-driven approach for benchmarking DFTB-approximate excited state methods

Published:2023 Issue:5 Volume:25 Page:3789-3798
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Bertoni Andrés I.¹^ORCID,Sánchez Cristián G.¹^ORCID

Affiliation:

1. Instituto Interdisciplinario de Ciencias Básicas (ICB-CONICET), Universidad Nacional de Cuyo, Padre Jorge Contreras 1300, Mendoza 5502, Argentina

Abstract

We propose a chemically-informed data-driven approach to benchmark approximate density-functional tight-binding (DFTB) excited state (ES) methods that are currently available within the DFTB+ suite, using data from a machine learning (ML) dataset.

Funder

Agencia Nacional de Promoción Científica y Tecnológica

Consejo Nacional de Investigaciones Científicas y Técnicas

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2023/CP/D2CP04979A

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