Work

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

Year: 2022

Type: article

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.

Source: Physical Chemistry Chemical Physics

DOI: 10.1039/d2cp04979a

Authors: Andrés I. Bertoni, Cristián G. Sánchez

Institutions: Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional de Cuyo, Instituto Interdisciplinario de Ciencias Básicas

Language: English

Topic: Advanced Chemical Physics Studies

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: Consejo Nacional de Investigaciones Científicas y Técnicas, Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación, Agencia Nacional de Promoción Científica y Tecnológica

Awards: G3139259226, G4359570771, G871012223