Revealing the reconstruction mechanism of AgPd nanoalloys under fluorination based on a multiscale deep learning potential (English)
- New search for: Guo, Longfei
- New search for: Shan, Shuang
- New search for: Liu, Xiaoqing
- New search for: Zhang, Wanxuan
- New search for: Xu, Peng
- New search for: Ma, Fanzhe
- New search for: Li, Zhen
- New search for: Wang, Chongyang
- New search for: Wang, Junpeng
- New search for: Chen, Fuyi
- New search for: Guo, Longfei
- New search for: Shan, Shuang
- New search for: Liu, Xiaoqing
- New search for: Zhang, Wanxuan
- New search for: Xu, Peng
- New search for: Ma, Fanzhe
- New search for: Li, Zhen
- New search for: Wang, Chongyang
- New search for: Wang, Junpeng
- New search for: Chen, Fuyi
In:
The Journal of Chemical Physics
;
160
, 17
;
16
;
2024
- Article (Journal) / Electronic Resource
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Title:Revealing the reconstruction mechanism of AgPd nanoalloys under fluorination based on a multiscale deep learning potential
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Additional title:Guo et al.
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Contributors:Guo, Longfei ( author ) / Shan, Shuang ( author ) / Liu, Xiaoqing ( author ) / Zhang, Wanxuan ( author ) / Xu, Peng ( author ) / Ma, Fanzhe ( author ) / Li, Zhen ( author ) / Wang, Chongyang ( author ) / Wang, Junpeng ( author ) / Chen, Fuyi ( author )
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Published in:The Journal of Chemical Physics ; 160, 17 ; 16
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Publisher:
- New search for: AIP Publishing LLC
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Publication date:2024-05-07
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Size:16 pages
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ISSN:
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DOI:
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Type of media:Article (Journal)
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Type of material:Electronic Resource
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Language:English
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Keywords:
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Source:
Table of contents – Volume 160, Issue 17
The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.
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Multiparticle collision dynamics simulations of hydrodynamic interactions in colloidal suspensions: How well does the discrete particle approach do at short range?Peng, Ying-Shuo / Sinno, Talid et al. | 2024
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Perspective: Atomistic simulations of water and aqueous systems with machine learning potentialsOmranpour, Amir / Montero De Hijes, Pablo / Behler, Jörg / Dellago, Christoph et al. | 2024
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Energy decomposition analysis method using density matrix formulationZhang, Yueyang / Yan, Longxiang / Wu, Wei / Su, Peifeng et al. | 2024
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