图卷积神经网络在晶体材料研发中的应用进展

doi:10.11871/jfdc.issn.2096-742X.2024.01.008

数据与计算发展前沿 ›› 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

图卷积神经网络在晶体材料研发中的应用进展

劳思思^1,²(),田子奇^2,^*()

1.宁波大学，浙江宁波 315211
2.中国科学院宁波材料技术与工程研究所，浙江宁波 315201

收稿日期:2022-09-22 出版日期:2024-02-20 发布日期:2024-02-21
通讯作者: * 田子奇（E-mail: tianziqi@nimte.ac.cn）
作者简介:劳思思，宁波大学，硕士研究生，现在中国科学院宁波材料技术与工程研究所联合培养。目前主要从事机器学习在催化领域的应用研究工作。
本文负责检索文献并完成文章写作。
LAO Sisi is a master student at Ningbo University. She is currently co-cultivated by the Ningbo Institute of Materials Technology and Engineering of the Chinese Academy of Sciences. She is mainly engaged in the application research of machine learning in the field of catalysis.
In this paper, she is responsible for completing the analysis of both domestic and international research reviews and finishing the manuscript.
E-mail: laosisi@nimte.ac.cn|田子奇，中国科学院宁波材料技术与工程研究所，研究员，博士，共计发表SCI论文70余篇，承担国家自然科学基金青年项目、面上项目以及浙江省杰出青年项目。主要从事气体分离与转化材料的理论研究工作。
本文负责指导并提出修改意见
TIAN Ziqi, Ph.D., is an associate researcher at Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences. He has published more than 70 SCI papers and has undertaken the National Natural Science Foundation Youth Project, General Project and Outstanding Youth Project of Zhejiang Province. His research mainly focuses on the theoretical investigation of novel materials for gas separation and conversion.
In this paper, he is responsible for the guidance of manuscript writing.
E-mail: tianziqi@nimte.ac.cn
基金资助:
国家自然科学基金面上项目(52171022)

Progress in application of Graph Convolutional Neural Network in Crystal Material Development

LAO Sisi^1,²(),TIAN Ziqi^2,^*()

1. Ningbo University, Ningbo, Zhejiang 315211, China
2. Ningbo Institute of Materials Technology & Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang 315201, China

Received:2022-09-22 Online:2024-02-20 Published:2024-02-21

摘要/Abstract

摘要：

【目的】探讨图卷积神经网络模型在晶体材料研究中的应用及未来可能的发展方向。【方法】首先介绍图卷积神经网络的发展历程及研究现状，然后对目前图卷积神经网络在晶体材料中的应用进行分类探讨，最后展望未来图卷积神经网络在晶体材料领域中的研究方向。【结果】图卷积神经网络在自然科学研究中得到越发广泛的应用，在晶体材料的结构设计和性能预测中展现出较好的效果。【结论】图卷积神经网络已逐渐应用于晶体材料的设计和预测，提高模型的泛化性以及如何提取深层特征，是图卷积神经网络未来的研究方向。

关键词: 图卷积神经网络, 晶体材料, 材料设计, 性能预测

Abstract:

[Objective] This paper discusses the application of graph convolutional neural network models in crystal materials research and its possible future development direction. [Methods] Firstly, the development and research status of graph convolutional networks are introduced. Secondly, the current application of graph convolutional networks in crystal materials is classified and discussed. Finally, the future research direction of graph convolutional neural networks in crystal materials is discussed. [Results] Graph convolutional neural network has been more and more widely used in natural science research and has shown good results in the structural design and performance prediction of crystal materials. [Conclusions] Graph convolutional neural network has been gradually applied to the design and prediction of crystal materials. Improving the generalization of models and extracting deep features are the future research direction of graph convolutional neural network.

Key words: graph convolutional neural network, crystal material, material design, performance prediction

劳思思, 田子奇. 图卷积神经网络在晶体材料研发中的应用进展[J]. 数据与计算发展前沿, 2024, 6(1): 79-93.

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.

图/表 8

图1

图2

图3

图4

图5

表1

各个图卷积网络模型比较"

		优点	缺点	应用任务	时间复杂度
基于频域	Spectral CNN^[4]	泛化到非欧式空间	整图计算，效率低	图分类	O(n³)
	ChebyNet^[5]	运算简便	参数较多，不灵活	节点分类	O(m)
	GWNN^[6]	参数量少，计算代价低	适应性差	节点分类	O(m)
	DGCNN^[7]	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^[19]	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： $v_{i}^{(t+1)}=v_{i}^{(t)}+\sum_{j, k} \sigma\left(z_{(i, j)_{k}}^{(t)} W_{f}^{(t)}+b_{f}^{(t)}\right) \odot g\left(z_{(i, j)_{k}}^{(t)} W_{s}^{(t)}+b_{s}^{(t)}\right) $ (1)
	MEGNet^[20]	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^[21]	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： $\sum_{j, l, k, k^{\prime}} \sigma\left( z_{{{\left( i,j,l \right)}_{k,{k}'}}}^{\text{ }\!\!'\!\!\text{ }\left( t \right)}W_{1}^{'\left( t \right)}+b_{1}^{'\left( t \right)} \right) \odot g\left( z_{{{\left( i,j,l \right)}_{k,k'}}}^{'\left( t \right)}W_{2}^{'\left( t \right)}+b_{2}^{'\left( t \right)} \right)$ (5)
	文献^[24]	参数少、速度快	只关注局部特征	USPTO	—
	GATGNN^[25]	考虑全局特征、精度高		Materials Project	相邻节点信息加权函数： $α i, j k = s o f t m a x g a i j k =$ $s o f t m a x (g (a T [W k v i k - 1 \| \| W k v j k - 1]))$ (6)

表1

图6

图7

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图卷积神经网络在晶体材料研发中的应用进展

Progress in application of Graph Convolutional Neural Network in Crystal Material Development

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