SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Advances in Neural Information Processing Systems

Volumn 2017-December, Issue , 2017, Pages 992-1002

  Schutt K T ^a   Kindermans, P J ^a   Sauceda, H E ^b   Chmiela, S ^a   Tkatchenko, A ^c   Muller K R ^a,d,e  

^a TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

^b FRITZ HABER INSTITUT DER MAX PLANCK GESELLSCHAFT   (Germany)

^c UNIVERSITY OF LUXEMBOURG   (Luxembourg)

^d MAX PLANCK INSTITUTE FOR INFORMATICS   (Germany)

^e Korea University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

BANDPASS FILTERS; BENCHMARKING; CONVOLUTION; MOLECULAR DYNAMICS; MOLECULES; NETWORK ARCHITECTURE; NEURAL NETWORKS; QUANTUM CHEMISTRY;

ATOMS IN MOLECULES; CONVOLUTIONAL NEURAL NETWORK; LEARNING ARCHITECTURE MODELS; MOLECULAR DYNAMICS TRAJECTORIES; PHYSICAL INFORMATION; QUANTUM INTERACTIONS; STATE-OF-THE-ART PERFORMANCE; STRUCTURAL VARIATIONS;

DEEP LEARNING;

EID: 85044257334     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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