Research on framework of nanocellulose molecular structure prediction model based on variational encoder
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.
deep learningnanocellulosestructure predictionneural networkmodel design
万方数据
维普
