计算机科学
图形
理论计算机科学
代表(政治)
节点(物理)
特征向量
人工神经网络
图形属性
Crystal(编程语言)
拓扑(电路)
算法
人工智能
折线图
数学
电压图
物理
组合数学
法学
程序设计语言
量子力学
政治
政治学
作者
Qian Li,Yanguang Zhou,Wei Zhou,Hao Deng,Huaijuan Zang,Zhao Niu,Zhan Shu,Yongsheng Ren,Jing Xu,Wenhui Ma
标识
DOI:10.1080/00268976.2024.2341959
摘要
The machine learning approach applied to materials discovery is a popular research direction. Knowledge of quantum chemistry explains that the structure of a material determines its properties. Graph neural networks (GNNs) provide a unique way of predicting the macroscopic properties of molecules and crystals rather than by solving the computationally expensive Schrödinger equation. Graph neural networks can abundantly transform the structural information of materials into corresponding features, and many models based on graph neural networks have been applied to predict material properties. We developed a new model (DYCGNN) containing a node update module for our designed edge-graph attention network composition. Through the application of the edge-gatv2 module, this module can effectively learn the complex relationship between nodes and neighbouring nodes in the crystal. Based on the calculated weight coefficients of each neighbouring node, the representation of the node is updated more effectively. In addition, we fuse the position information of the nodes into the node eigenvectors to complement the spatial information of the crystal and enrich the complete representation of the crystal. As we investigate the DYCGNN model, we find that our approach can outperform the predictions of previous models and provide insights into material crystallization.
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