Abstract:

Nanocellulose exhibits rich molecular structures and properties due to its diverse raw materials, preparation and modification methods. However, due to its structural diversity, the research and development cycle under traditional methods is long and cost is high. If the molecular structure can be designed from the micro scale, it will help to significantly shorten the cycle. At present, the existing molecular structure prediction models are mostly suitable for inorganic materials and have limited adaptability to nanocellulose. Based on the structural characteristics of nanocellulose, four unique structure generation constraints were designed. The results show that the structure generation accuracy of the nanocellulose molecular structure prediction model built based on the variational encoder reaches approximately 63.0%. The model performs well in identifying partial structures, with a recognition rate of 87.0% for the main structure. It can effectively decouple the main structure of nanocellulose and the modified group structure, and proves to a certain extent that the model framework proposed in this study can effectively decouple the nanocellulose main structure and modified group structure. The structure prediction of cellulose and its derivative materials is feasible and helps to assist the development and preparation of related materials.

Key words: deep learning, nanocellulose, structure prediction, neural network, model design

摘要：

纳米纤维素因其多样化的原料、制备方法以及改性方法而展现出丰富的分子结构及性能。但正因其结构多样性，在传统方法下研发周期长，研发成本高，若能从微观尺度设计分子结构则有助于大幅缩短该周期，而目前，现有的分子结构预测模型多适用于无机材料，对纳米纤维素的适应性有限。基于变分编码器搭建了纳米纤维素分子结构预测模型，针对纳米纤维素结构特点，设计了4条独有的结构生成约束。模型的结构生成准确率达到约63.0%。模型在识别部分结构方面表现优异，对主体结构识别率达到87.0%，能有效解耦纳米纤维素主体结构与改性基团结构，并在一定程度上证明了提出的模型框架对纳米纤维素及衍生材料的结构预测具有可行性，有助于相关材料的研发与制备。

关键词: 深度学习, 纳米纤维素, 结构预测, 神经网络, 模型设计