Data-driven approach for benchmarking DFTB-approximate excited state methods
Author:
Affiliation:
1. Instituto Interdisciplinario de Ciencias Básicas (ICB-CONICET), Universidad Nacional de Cuyo, Padre Jorge Contreras 1300, Mendoza 5502, Argentina
Abstract
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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1. Electron density learning of non-covalent systems
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4. Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules
5. Electronic spectra from TDDFT and machine learning in chemical space
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