Understanding machine-learned density functionals-Reference-Cited by-同舟云学术

Understanding machine-learned density functionals

Published:2015-11-17 Issue:11 Volume:116 Page:819-833
ISSN:0020-7608
Container-title:International Journal of Quantum Chemistry
language:en
Short-container-title:Int. J. Quantum Chem.

Author:

Li Li¹,Snyder John C.²³,Pelaschier Isabelle M.¹⁴,Huang Jessica⁵,Niranjan Uma-Naresh⁶,Duncan Paul⁵,Rupp Matthias⁷,Müller Klaus-Robert²⁸,Burke Kieron¹⁵

Affiliation:

1. Department of Physics and Astronomy; University of California; Irvine California 92697

2. Machine Learning Group; Technical University of Berlin; 10587 Germany

3. Max Planck Institute of Microstructure Physics; Weinberg 2 Halle Saale 06120 Germany

4. Department of Physics; Vanderbilt University; Nashville Tennessee 37235

5. Department of Chemistry; University of California; Irvine California 92697

6. Department of Computer Science; University of California; Irvine California 92697

7. Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials (MARVEL), Department of Chemistry, University of Basel CH-4056, Switzerland Present address: Fritz Haber Institute of the Max Planck Society, 14195 Berlin, Germany

8. Department of Brain and Cognitive Engineering; Korea University; Anam-Dong, Seongbuk-Gu Seoul 136-713 Korea

Funder

NSF

SNF

Publisher

Wiley

Subject

Physical and Theoretical Chemistry,Condensed Matter Physics,Atomic and Molecular Physics, and Optics

Reference71 articles.

1. Quantum mechanics of many-electron systems

2. Nobel Lecture: Electronic structure of matter—wave functions and density functionals

3. Inhomogeneous Electron Gas

4. Density Functional Theory

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