Neural message passing for quantum chemistry

34th International Conference on Machine Learning, ICML 2017

Volumn 3, Issue , 2017, Pages 2053-2070

  Gilmer, Justin ^a   Schoenholz, Samuel S ^a   Riley, Patrick F ^a   Vinyals, Oriol ^b   Dahl, George E ^a  

^a GOOGLE INC   (United States)

^b DEEPMIND   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS; MOLECULES; QUANTUM CHEMISTRY;

DRUG DISCOVERY; GROUND TRUTH; INPUT GRAPHS; MESSAGE PASSING ALGORITHM; MOLECULAR PROPERTIES; MOLECULAR SYMMETRY; NEURAL NETWORK MODEL; STATE OF THE ART;

MESSAGE PASSING;

EID: 85045254838     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (4)

1. Bruna, Joan, Zaremba, Wojciech, Szlam, Arthur, and LeCun, Yann. Spectral networks and locally connected networks on graphs. ArXiv preprint arXiv: 1312.6203,2013
2. Defferrard, Michael, Bresson, Xavier, and Vandergheynst, Pierre. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems, pp. 3837-3845, 2016
3. Fabcr, Felix, Hutchison, Luke, Huang, Bing, Gilmer, Justin, Schocnholz, Samuel S., Dahl, George E., Vinyals, Oriol, Kcarncs, Steven, Riley, Patrick F., and von Lilienfeld, O. Anatole. Fast machine learning models of electronic and energetic properties consistently reach approximation errors better than dft accuracy. Https://arxiv.org/abs/!702.05532, 2017
4. Kipf, T. N. And Welling, M. Semi-Supervised Classification with Graph Convolutional Networks. ArXiv e-prints, September 2016.