E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Simon Batzner; Albert Musaelian; Lixin Sun; Mario Geiger; Jonathan P. Mailoa; Mordechai Kornbluth; Nicola Molinari; Tess Smidt; Boris Kozinsky

doi:10.1038/s41467-022-29939-5

Keywords

Equivariant mapComputer scienceInvariant (physics)Artificial neural networkRepresentation (politics)Ab initioSet (abstract data type)Theoretical computer scienceStatistical physicsAlgorithmArtificial intelligencePhysicsQuantum mechanicsMathematics

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Publication Info

Year: 2022
Type: article
Volume: 13
Issue: 1
Pages: 2453-2453
Citations: 1318
Access: Closed

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APA Style

                            
                                
                                    Simon Batzner, 
                                
                                    Albert Musaelian, 
                                
                                    Lixin Sun
                                
                                et al.
                            
                            (2022). 
                            E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. 
                            Nature Communications
                            , 13
                            (1)
                            , 2453-2453.
                            https://doi.org/10.1038/s41467-022-29939-5
                        

Identifiers

DOI: 10.1038/s41467-022-29939-5