Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph da...

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Detalles Bibliográficos
Autores principales: Choi, Jong Youl, Zhang, Pei, Mehta, Kshitij, Blanchard, Andrew, Lupo Pasini, Massimiliano
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575242/
https://www.ncbi.nlm.nih.gov/pubmed/36253845
http://dx.doi.org/10.1186/s13321-022-00652-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575242/
https://www.ncbi.nlm.nih.gov/pubmed/36253845
http://dx.doi.org/10.1186/s13321-022-00652-1

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