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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...
| Autores principales: | , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier
2022
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| Materias: | |
| 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 |