DICP OpenIR
Subject Area物理化学
Communication: Separable potential energy surfaces from multiplicative artificial neural networks
Koch, Werner1; Zhang, Dong H.; KochWerner
Source PublicationJOURNAL OF CHEMICAL PHYSICS
2014-07-01
DOI10.1063/1.4887508
Volume141Issue:2Pages:021101
Indexed BySCI
SubtypeArticle
WOS HeadingsScience & Technology ; Physical Sciences
WOS SubjectPhysics, Atomic, Molecular & Chemical
WOS Research AreaPhysics
WOS KeywordCONFIGURATION GAUSSIAN WAVEPACKETS ; QUANTUM DYNAMICS ; FEEDFORWARD NETWORKS ; AB-INITIO ; INTERPOLATION ; ALGORITHM
AbstractWe present a potential energy surface fitting scheme based on multiplicative artificial neural networks. It has the sum of products form required for efficient computation of the dynamics of multidimensional quantum systems with the multi configuration time dependent Hartree method. Moreover, it results in analytic potential energy matrix elements when combined with quantum dynamics methods using Gaussian basis functions, eliminating the need for a local harmonic approximation. Scaling behavior with respect to the complexity of the potential as well as the requested accuracy is discussed. (C) 2014 AIP Publishing LLC.
Language英语
WOS IDWOS:000340269200001
Citation statistics
Document Type期刊论文
Identifierhttp://cas-ir.dicp.ac.cn/handle/321008/144298
Collection中国科学院大连化学物理研究所
Corresponding AuthorKochWerner
Affiliation1.Chinese Acad Sci, Dalian Inst Chem Phys, State Key Lab Mol React Dynam, Dalian, Peoples R China
2.Chinese Acad Sci, Dalian Inst Chem Phys, Ctr Theoret Computat Chem, Dalian, Peoples R China
Recommended Citation
GB/T 7714
Koch, Werner,Zhang, Dong H.,KochWerner. Communication: Separable potential energy surfaces from multiplicative artificial neural networks[J]. JOURNAL OF CHEMICAL PHYSICS,2014,141(2):021101.
APA Koch, Werner,Zhang, Dong H.,&KochWerner.(2014).Communication: Separable potential energy surfaces from multiplicative artificial neural networks.JOURNAL OF CHEMICAL PHYSICS,141(2),021101.
MLA Koch, Werner,et al."Communication: Separable potential energy surfaces from multiplicative artificial neural networks".JOURNAL OF CHEMICAL PHYSICS 141.2(2014):021101.
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