{"id":"W2778051509","doi":"10.1063/1.5019779","title":"SchNet – A deep learning architecture for molecules and materials","year":2018,"lang":"en","type":"article","venue":"The Journal of Chemical Physics","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":2194,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Banting and Best Diabetes Centre, University of Toronto; National Research Foundation of Korea; H2020 Marie Skłodowska-Curie Actions; Bundesministerium für Bildung und Forschung; National Research Foundation; Deutsche Forschungsgemeinschaft; H2020 European Research Council; Ministry of Science, ICT and Future Planning; European Commission","keywords":"Deep learning; Chemical space; Computer science; Artificial intelligence; Ab initio; Molecular dynamics; Architecture; Space (punctuation); Quantum; Physics; Quantum mechanics; Bioinformatics; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null}