A deep equivariant neural network approach for efficient hybrid density functional calculations.
Saved in:
| Title: | A deep equivariant neural network approach for efficient hybrid density functional calculations. |
|---|---|
| Authors: | Tang Z; State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, 100084, Beijing, China., Li H; State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, 100084, Beijing, China.; Institute for Advanced Study, Tsinghua University, 100084, Beijing, China., Lin P; Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China.; Songshan Lake Materials Laboratory, 523808, Dongguan, Guangdong, China.; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, 230026, Hefei, Anhui, China., Gong X; State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, 100084, Beijing, China.; School of Physics, Peking University, 100871, Beijing, China., Jin G; Key Laboratory of Quantum Information, University of Science and Technology of China, 230026, Hefei, Anhui, China., He L; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, 230026, Hefei, Anhui, China.; Key Laboratory of Quantum Information, University of Science and Technology of China, 230026, Hefei, Anhui, China., Jiang H; College of Chemistry and Molecular Engineering, Peking University, 100871, Beijing, China., Ren X; Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China. renxg@iphy.ac.cn.; Songshan Lake Materials Laboratory, 523808, Dongguan, Guangdong, China. renxg@iphy.ac.cn., Duan W; State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, 100084, Beijing, China. duanw@tsinghua.edu.cn.; Institute for Advanced Study, Tsinghua University, 100084, Beijing, China. duanw@tsinghua.edu.cn.; Frontier Science Center for Quantum Information, Beijing, China. duanw@tsinghua.edu.cn., Xu Y; State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, 100084, Beijing, China. yongxu@mail.tsinghua.edu.cn.; Frontier Science Center for Quantum Information, Beijing, China. yongxu@mail.tsinghua.edu.cn.; RIKEN Center for Emergent Matter Science (CEMS), Wako, Saitama, 351-0198, Japan. yongxu@mail.tsinghua.edu.cn. |
| Source: | Nature communications [Nat Commun] 2024 Oct 11; Vol. 15 (1), pp. 8815. Date of Electronic Publication: 2024 Oct 11. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE; PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: mdl DbLabel: MEDLINE Ultimate An: 39394190 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: A deep equivariant neural network approach for efficient hybrid density functional calculations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Tang+Z%22">Tang Z</searchLink>; State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, 100084, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Li+H%22">Li H</searchLink>; State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, 100084, Beijing, China.; Institute for Advanced Study, Tsinghua University, 100084, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Lin+P%22">Lin P</searchLink>; Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China.; Songshan Lake Materials Laboratory, 523808, Dongguan, Guangdong, China.; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, 230026, Hefei, Anhui, China.<br /><searchLink fieldCode="AU" term="%22Gong+X%22">Gong X</searchLink>; State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, 100084, Beijing, China.; School of Physics, Peking University, 100871, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Jin+G%22">Jin G</searchLink>; Key Laboratory of Quantum Information, University of Science and Technology of China, 230026, Hefei, Anhui, China.<br /><searchLink fieldCode="AU" term="%22He+L%22">He L</searchLink>; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, 230026, Hefei, Anhui, China.; Key Laboratory of Quantum Information, University of Science and Technology of China, 230026, Hefei, Anhui, China.<br /><searchLink fieldCode="AU" term="%22Jiang+H%22">Jiang H</searchLink>; College of Chemistry and Molecular Engineering, Peking University, 100871, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Ren+X%22">Ren X</searchLink>; Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China. renxg@iphy.ac.cn.; Songshan Lake Materials Laboratory, 523808, Dongguan, Guangdong, China. renxg@iphy.ac.cn.<br /><searchLink fieldCode="AU" term="%22Duan+W%22">Duan W</searchLink>; State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, 100084, Beijing, China. duanw@tsinghua.edu.cn.; Institute for Advanced Study, Tsinghua University, 100084, Beijing, China. duanw@tsinghua.edu.cn.; Frontier Science Center for Quantum Information, Beijing, China. duanw@tsinghua.edu.cn.<br /><searchLink fieldCode="AU" term="%22Xu+Y%22">Xu Y</searchLink>; State Key Laboratory of Low Dimensional Quantum Physics and Department of Physics, Tsinghua University, 100084, Beijing, China. yongxu@mail.tsinghua.edu.cn.; Frontier Science Center for Quantum Information, Beijing, China. yongxu@mail.tsinghua.edu.cn.; RIKEN Center for Emergent Matter Science (CEMS), Wako, Saitama, 351-0198, Japan. yongxu@mail.tsinghua.edu.cn. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22101528555%22">Nature communications</searchLink> [Nat Commun] 2024 Oct 11; Vol. 15 (1), pp. 8815. <i>Date of Electronic Publication: </i>2024 Oct 11. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Nature+Pub%2E+Group%22">Nature Pub. Group </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>101528555 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2041-1723 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2220411723%22">20411723 </searchLink><i>NLM ISO Abbreviation: </i>Nat Commun <i>Subsets: </i>MEDLINE; PubMed not MEDLINE |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=39394190 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1038/s41467-024-53028-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 8815 Titles: – TitleFull: A deep equivariant neural network approach for efficient hybrid density functional calculations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tang Z – PersonEntity: Name: NameFull: Li H – PersonEntity: Name: NameFull: Lin P – PersonEntity: Name: NameFull: Gong X – PersonEntity: Name: NameFull: Jin G – PersonEntity: Name: NameFull: He L – PersonEntity: Name: NameFull: Jiang H – PersonEntity: Name: NameFull: Ren X – PersonEntity: Name: NameFull: Duan W – PersonEntity: Name: NameFull: Xu Y IsPartOfRelationships: – BibEntity: Dates: – D: 11 M: 10 Text: 2024 Oct 11 Type: published Y: 2024 Identifiers: – Type: issn-electronic Value: 2041-1723 Numbering: – Type: volume Value: 15 – Type: issue Value: 1 Titles: – TitleFull: Nature communications Type: main |
| ResultId | 1 |