Frontiers of Data and Computing ›› 2024, Vol. 6 ›› Issue (1): 79-93.
CSTR: 32002.14.jfdc.CN10-1649/TP.2024.01.008
doi: 10.11871/jfdc.issn.2096-742X.2024.01.008
• Technology and Application • Previous Articles Next Articles
Received:
2022-09-22
Online:
2024-02-20
Published:
2024-02-21
LAO Sisi, TIAN Ziqi. Progress in application of Graph Convolutional Neural Network in Crystal Material Development[J]. Frontiers of Data and Computing, 2024, 6(1): 79-93, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2024.01.008.
Table 1
Comparison of various graph convolutional network models"
优点 | 缺点 | 应用任务 | 时间复杂度 | ||
---|---|---|---|---|---|
基于 频域 | Spectral CNN[ | 泛化到非欧式空间 | 整图计算, 效率低 | 图分类 | O(n3) |
ChebyNet [ | 运算简便 | 参数较多, 不灵活 | 节点分类 | O(m) | |
GWNN[ | 参数量少,计算代价低 | 适应性差 | 节点分类 | O(m) | |
DGCNN[ | 特征抽象能力强 | 没有全局 特征 | 图分类 | — | |
优点 | 缺点 | 应用数据集 | 相关函数 | ||
基于空间域(材料领域) | CGCNN[ | 灵活,搜索速度快 | 邻居节点数固定,编码信息单一 | Materials Project、OQMD | 节点向量更新: |
MEGNet[ | 可解释性、可组合性强 | 仅注重局部原子特点 | QM9 | 边缘更新: $e^{\prime}{ }_{k}=\phi_{e}\left(v_{s k} \bigoplus v_{r k} \bigoplus e_{k} \bigoplus u\right)$(2)节点更新:$v_{i}^{\prime}=\phi_{v}\left(\bar{v}_{i}^{e} \oplus v_{i} \bigoplus u\right)$(3) 全局向量更新:$u^{\prime}=\Phi_{u}\left(\bar{u}^{e} \bigoplus \bar{u}^{v} \bigoplus u\right)$(4) | |
iCGCNN[ | 识别率大,成功率高,搜索速度快 | 适用结构单一 | OQMD | 节点向量更新添加函数: | |
文献[ | 参数少、 速度快 | 只关注局部 特征 | USPTO | — | |
GATGNN[ | 考虑全局特征、精度高 | Materials Project | 相邻节点信息加权函数: |
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