Learning to Approximate Density Functionals-Reference-Cited by-同舟云学术

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Learning to Approximate Density Functionals

Published:2021-02-03 Issue:4 Volume:54 Page:818-826
ISSN:0001-4842
Container-title:Accounts of Chemical Research
language:en
Short-container-title:Acc. Chem. Res.

Author:

Kalita Bhupalee¹^ORCID,Li Li²,McCarty Ryan J.¹,Burke Kieron¹³^ORCID

Affiliation:

1. Department of Chemistry, University of California, Irvine, California 92697, United States

2. Google Research, Mountain View, California 94043, United States

3. Department of Physics and Astronomy, University of California, Irvine, California 92697, United States

Funder

Division of Chemistry

Division of Graduate Education

Publisher

American Chemical Society (ACS)

Subject

General Medicine,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.accounts.0c00742

Reference57 articles.

1. Finding Density Functionals with Machine Learning

2. Bypassing the Kohn-Sham equations with machine learning

3. Pure density functional for strong correlation and the thermodynamic limit from machine learning

4. Kohn-Sham Equations as Regularizer: Building Prior Knowledge into Machine-Learned Physics

5. Perspective on density functional theory

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