Machine learning models of the energy curvature vs particle number for optimal tuning of long-range corrected functionals-Reference-Cited by-同舟云学术

Machine learning models of the energy curvature vs particle number for optimal tuning of long-range corrected functionals

Published:2020-04-21 Issue:15 Volume:152 Page:154103
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Fabrizio Alberto¹²^ORCID,Meyer Benjamin¹²,Corminboeuf Clemence¹²^ORCID

Affiliation:

1. Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland

2. National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

Funder

The Swiss National Science Foundation, NCCR MARVEL

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://aip.scitation.org/doi/pdf/10.1063/5.0005039

Reference127 articles.

1. Inhomogeneous Electron Gas

2. Density-Functional Theory for Fractional Particle Number: Derivative Discontinuities of the Energy

3. Insights into Current Limitations of Density Functional Theory