Scalable deeper graph neural networks for high-performance materials property prediction

Machine-learning-based materials property prediction models have emerged as a promising approach for new materials discovery, among which the graph neural networks (GNNs) have shown the best performance due to their capability to learn high-level features from crystal structures. However, existing G...

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Detalles Bibliográficos
Autores principales: Omee, Sadman Sadeed, Louis, Steph-Yves, Fu, Nihang, Wei, Lai, Dey, Sourin, Dong, Rongzhi, Li, Qinyang, Hu, Jianjun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9122959/
https://www.ncbi.nlm.nih.gov/pubmed/35607621
http://dx.doi.org/10.1016/j.patter.2022.100491

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9122959/
https://www.ncbi.nlm.nih.gov/pubmed/35607621
http://dx.doi.org/10.1016/j.patter.2022.100491

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