Machine Learning Approaches toward Orbital-free Density Functional Theory: Simultaneous Training on the Kinetic Energy Density Functional and Its Functional Derivative-Reference-Cited by-同舟云学术

Machine Learning Approaches toward Orbital-free Density Functional Theory: Simultaneous Training on the Kinetic Energy Density Functional and Its Functional Derivative

Published:2020-08-10 Issue:9 Volume:16 Page:5685-5694
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Meyer Ralf¹^ORCID,Weichselbaum Manuel¹,Hauser Andreas W.¹^ORCID

Affiliation:

1. Institute of Experimental Physics, Graz University of Technology, Petersgasse 16, 8010 Graz, Austria

Funder

Austrian Science Fund

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

http://pubs.acs.org/doi/pdf/10.1021/acs.jctc.0c00580

Reference75 articles.

1. Fourteen easy lessons in density functional theory

2. Perspective: Fifty years of density-functional theory in chemical physics

3. Inhomogeneous Electron Gas

4. Self-Consistent Equations Including Exchange and Correlation Effects

5. The calculation of atomic fields

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