Potential energy surfaces from high fidelity fitting of ab initio points: the permutation invariant polynomial - neural network approach

International Reviews in Physical Chemistry

Volumn 35, Issue 3, 2016, Pages 479-506

  Jiang, Bin ^a   Li, Jun ^b   Guo, Hua ^c  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b CHONGQING UNIVERSITY   (China)

^c UNIVERSITY OF NEW MEXICO   (United States)

Author keywords

neural networks; permutation symmetry; potential energy surfaces; reaction dynamics

Indexed keywords

EID: 84985987220     PISSN: 0144235X     EISSN: 1366591X     Source Type: Journal    
DOI: 10.1080/0144235X.2016.1200347     Document Type: Review

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