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Enhancing robustness and efficiency of density matrix embedding theory via semidefinite programming and local correlation potential fitting

Publication ,  Journal Article
Wu, X; Lindsey, M; Zhou, T; Tong, Y; Lin, L
Published in: Physical Review B
August 12, 2020

Duke Scholars

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Published In

Physical Review B

DOI

EISSN

2469-9969

ISSN

2469-9950

Publication Date

August 12, 2020

Volume

102

Issue

8

Publisher

American Physical Society (APS)

Related Subject Headings

  • 51 Physical sciences
  • 40 Engineering
  • 34 Chemical sciences
 

Citation

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Wu, X., Lindsey, M., Zhou, T., Tong, Y., & Lin, L. (2020). Enhancing robustness and efficiency of density matrix embedding theory via semidefinite programming and local correlation potential fitting. Physical Review B, 102(8). https://doi.org/10.1103/physrevb.102.085123
Wu, Xiaojie, Michael Lindsey, Tiangang Zhou, Yu Tong, and Lin Lin. “Enhancing robustness and efficiency of density matrix embedding theory via semidefinite programming and local correlation potential fitting.” Physical Review B 102, no. 8 (August 12, 2020). https://doi.org/10.1103/physrevb.102.085123.
Wu, Xiaojie, et al. “Enhancing robustness and efficiency of density matrix embedding theory via semidefinite programming and local correlation potential fitting.” Physical Review B, vol. 102, no. 8, American Physical Society (APS), Aug. 2020. Crossref, doi:10.1103/physrevb.102.085123.
Wu X, Lindsey M, Zhou T, Tong Y, Lin L. Enhancing robustness and efficiency of density matrix embedding theory via semidefinite programming and local correlation potential fitting. Physical Review B. American Physical Society (APS); 2020 Aug 12;102(8).

Published In

Physical Review B

DOI

EISSN

2469-9969

ISSN

2469-9950

Publication Date

August 12, 2020

Volume

102

Issue

8

Publisher

American Physical Society (APS)

Related Subject Headings

  • 51 Physical sciences
  • 40 Engineering
  • 34 Chemical sciences