Incorporation of density scaling constraint in density functional design <i>via</i> contrastive representation learning-Reference-Cited by-同舟云学术

Incorporation of density scaling constraint in density functional design via contrastive representation learning

Published:2023 Issue:5 Volume:2 Page:1404-1413
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Gong Weiyi¹,Sun Tao²,Bai Hexin³,Chowdhury Shah Tanvir ur Rahman⁴,Chu Peng³,Aryal Anoj¹,Yu Jie⁵,Ling Haibin²,Perdew John P.⁵⁶,Yan Qimin¹^ORCID

Affiliation:

1. Department of Physics, Northeastern University, Boston, MA 02115, USA

2. Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA

3. Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA

4. Department of Materials Science, Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA

5. Department of Physics, Temple University, Philadelphia, PA 19122, USA

6. Department of Chemistry, Temple University, Philadelphia, PA 19122, USA

Abstract

We demonstrate that contrastive representation learning is a computationally efficient and flexible method to incorporate physical constraints, especially those defined by equalities, in machine-learning-based density functional design.

Funder

National Science Foundation

U.S. Department of Energy

Brookhaven National Laboratory

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D3DD00114H

Reference44 articles.

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5. Workhorse Semilocal Density Functional for Condensed Matter Physics and Quantum Chemistry

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