Equivariant Graph Neural Network for Crystalline Materials

Date: July 24, 2022

Location: 1st International Workshop on Spatio-Temporal Reasoning and Learning

This talk discusses the challenge of representing periodic materials and building equivariant machine learning models.

How to cite:

@inproceedings{klipfel_equ_strl_2022,
  author       = {Astrid Klipfel and Zied Bouraoui and Ya{\"{e}}l Fr{\'{e}}gier and Adlane Sayede},
  editor       = {Michael Sioutis and Zhiguo Long and John G. Stell and Jochen Renz},
  title        = {Equivariant Graph Neural Network for Crystalline Materials (Invited Paper)},
  booktitle    = {Proceedings of the 1st International Workshop on Spatio-Temporal Reasoning and Learning ({STRL} 2022), Vienna, Austria, July 24, 2022},
  series       = {CEUR Workshop Proceedings},
  volume       = {3190},
  publisher    = {CEUR-WS.org},
  year         = {2022},
  url          = {https://ceur-ws.org/Vol-3190/invited1.pdf},
}