基于图神经网络的高温树脂材料预测模型  

High-Temperature Resin Material Prediction Model Based on Graph Neural Network

作　　者：杨宁[1] 魏伟[1] 胡航语 郭雷[1] 方俊[1] YANG Ning;WEI Wei;HU Hangyu;GUO Lei;FANG Jun(School of Automation,Northwestern Polytechnical University,Xi'an 710129,China;School of Computer Science,Northwestern Polytechnical University,Xi'an 710129,China)

机构地区：[1]西北工业大学自动化学院,西安710129 [2]西北工业大学计算机学院,西安710129

出　　处：《功能高分子学报》2021年第6期554-561,共8页Journal of Functional Polymers

摘　　要：基于机器学习预测化合物性能的方法在材料研发的虚拟筛选中发挥着重要作用。现有方法通过人工提取特征构建传统机器学习模型,存在着特征提取困难以及难以处理简化分子线性输入(SMILES)码等问题。为了解决这些问题,本文提出了一种端到端的图神经网络预测树脂材料高温性能的方法。首先将树脂材料的SMILES码表示为图形,其中顶点代表原子,边代表化学键;然后通过构建分子图的图神经网络得到分子的向量表示;最后通过构建分子向量、环境、条件等信息的全连接神经网络回归模型预测树脂材料质量损失5%的最高温度。树脂材料数据集的实验表明,相较于传统的机器学习模型,端到端的图神经网络模型的预测准确率提升了一倍多。The method of predicting the properties of compounds based on machine learning plays an important role in the virtual screening of materials discovery.Existing methods of traditional machine learning models have to manually extract features,which have problems such as difficulty in extract features and difficulty in processing Simplified Molecular Input Line Entry System(SMILES)codes.In order to solve these problems,this paper proposes an end-to-end graph neural network to predict the high temperature performance of resin materials.First,the SMILES code of the resin material is represented as a graph,where the vertices are atoms and the edges are chemical bonds.Then,the vector representation of the molecule is obtained by constructing the graph neural network of the molecular graph.Finally,a full connection neural network contains information such as molecular vector,environment,and conditions to predict the maximum temperature by regression model.Experiments of the resin material data set show that the end-to-end graph neural network model proposed in this paper has more than doubled the accuracy of model prediction compared with the traditional machine learning models.

关 键 词：图神经网络 树脂材料 高温 性能 预测 

分 类 号：O6[理学—化学]