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Predicting mechanical properties of CO2hydrates: machine learning insights from molecular dynamics simulations.
Zhang, Yu; Song, Zixuan; Lin, Yanwen; Shi, Qiao; Hao, Yongchao; Fu, Yuequn; Wu, Jianyang; Zhang, Zhisen.
Affiliation
  • Zhang Y; Department of Physics, Research Institute for Biomimetics and Soft Matter, Jiujiang Research Institute and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, People's Republic of China.
  • Song Z; Department of Physics, Research Institute for Biomimetics and Soft Matter, Jiujiang Research Institute and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, People's Republic of China.
  • Lin Y; Department of Physics, Research Institute for Biomimetics and Soft Matter, Jiujiang Research Institute and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, People's Republic of China.
  • Shi Q; Department of Physics, Research Institute for Biomimetics and Soft Matter, Jiujiang Research Institute and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, People's Republic of China.
  • Hao Y; Department of Physics, Research Institute for Biomimetics and Soft Matter, Jiujiang Research Institute and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, People's Republic of China.
  • Fu Y; PoreLab, The Njord Centre, Department of Physics, University of Oslo, Oslo 0588, Norway.
  • Wu J; Department of Physics, Research Institute for Biomimetics and Soft Matter, Jiujiang Research Institute and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, People's Republic of China.
  • Zhang Z; NTNU Nanomechanical Lab, Norwegian University of Science and Technology (NTNU), Trondheim 7491, Norway.
J Phys Condens Matter ; 36(1)2023 Sep 27.
Article in En | MEDLINE | ID: mdl-37714183
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: J Phys Condens Matter Journal subject: BIOFISICA Year: 2023 Document type: Article Country of publication: United kingdom
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: J Phys Condens Matter Journal subject: BIOFISICA Year: 2023 Document type: Article Country of publication: United kingdom