使用图形卷积网络预测化富勒烯的结合能和电子性质
Linwei Sai1, Li Fu2, Jijun Zhao2
1Department of Mathematics, Hohai University, Changzhou 213200, China.
Journal of chemical information and modeling
|December 20, 2023
概括
研究人员开发了一个图形卷积网络 (GCN) 来预测化集群结构和能量. 这种计算方法准确地模拟了能量和电子属性,减少了对广泛密度函数理论 (DFT) 计算的需求.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 化集群,对碳富勒烯的异电子,表现出复杂的结构.
- 确定这些集群的基本状态结构是计算密集的,因为异构体随着大小的快速增加.
- 密度函数理论 (DFT) 的计算对于大型系统来说至关重要但耗时.
研究的目的:
- 开发一个图形卷积网络 (GCN) 以根据它们的拓学来预测化的能量特性.
- 为了降低与识别化集群的基态结构相关的计算成本.
- 探索GCNs在预测分子的电子性质中的应用.
主要方法:
- 一个图形卷积网络 (GCN) 的设计是使用从双重多面体上邻近度的变换中得出的顶点特征向量.
- GCN使用了两个图形卷积层来聚合双重多面体的局部特征.
- 该模型在 (BN) 28数据上进行了训练,并在 (BN) 23和 (BN) 24数据集上进行了验证,并对n=25-32进行了进一步的预测.
主要成果:
- 与 (BN) 23和 (BN) 24集群的DFT计算相比,GCN准确地复制了异构体的能量排序.
- 在n=25-32大小范围内的化集群的基态结构的预测与DFT结果有很好的一致性.
- GCN框架成功地被调整为预测 (BN) 28同位素的最高占用或最低未占用轨道能量.
结论:
- 开发的图形卷积网络提供了一个有效的方法来预测化子的能量和电子特性.
- 这种方法在集群的拓连接与其物理属性之间建立了直接联系.
- GCN框架为加速材料发现和理解复杂分子系统提供了一个有前途的计算工具.
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