Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity

Graph neural networks (GNNs) have recently been used to learn the representations of crystal structures through an end-to-end data-driven approach. However, a systematic top-down approach to evaluate and understand the limitations of GNNs in accurately capturing crystal structures has yet to be esta...

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Detalles Bibliográficos
Autores principales: Gong, Sheng, Yan, Keqiang, Xie, Tian, Shao-Horn, Yang, Gomez-Bombarelli, Rafael, Ji, Shuiwang, Grossman, Jeffrey C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2023
Materias:
Physical and Materials Sciences
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10637739/
https://www.ncbi.nlm.nih.gov/pubmed/37948518
http://dx.doi.org/10.1126/sciadv.adi3245

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10637739/
https://www.ncbi.nlm.nih.gov/pubmed/37948518
http://dx.doi.org/10.1126/sciadv.adi3245

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