A geometric-information-enhanced crystal graph network for predicting properties of materials
Graph neural networks are an accurate machine learning-based approach for property prediction. Here, a geometric-information-enhanced crystal graph neural network is demonstrated, which accurately predicts the formation energy and band gap of crystalline materials.
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/7942b7f2b0a14299851df024aa50898d |
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