文献类型：期刊文献

英文题名：A knowledge-guided neural network framework for accelerated design of composite materials: Application in high-performance biodegradable shape memory foams

作者：Gong, Zheng[1];Tian, Zhou[1,2];Lu, Jingyi[1,2];Zhao, Ling[3];Qian, Feng[1]

机构：[1]East China Univ Sci & Technol, State Key Lab Ind Control Technol, Shanghai 200237, Peoples R China;[2]Qingyuan Innovat Lab, Fujian 362801, Quanzhou, Peoples R China;[3]East China Univ Sci & Technol, Sch Chem Engn, Shanghai Key Lab Multiphase Mat Chem Engn, State Key Lab Chem Engn & Low Carbon Technol, Shanghai 200237, Peoples R China

年份：2026

卷号：357

外文期刊名：POLYMER

收录：;EI(收录号：20261920691574);WOS:【SCI-EXPANDED(收录号:WOS:001758508200001)】；

基金：This work is supported in part by Key Project of Science and Technology Innovation 2030, Grant/Award Number: 2023ZD0121001 , National Natural Science Foundation of China (62394343, 62394345) , the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017 and Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Shape memory; Biodegradable; Polymer foams; Neural network

摘要：A novel knowledge-guided neural network framework is proposed to accelerate the design of biodegradable functional materials. By transforming domain expertise from static prior knowledge into embeddable architectural components and constraint conditions, the model is enabled to proceed from a foundation of domain cognition. The knowledge-guided neural network framework was applied to high-performance, recyclable, reprocessable and biodegradable shape memory poly(butylene succinate-butylene terephthalate) (PBST)/polylactic acid (PLA) foams prepared using supercritical foaming technology. By matching the knowledge-guided neural network framework with the black-box characteristics of the foaming process-property relationship, the correlations among processing, structure, and performance of foams are effectively revealed. By using measurable morphological features as intermediate predictive outputs, the model's interpretability is enhanced. A multi-objective optimization genetic algorithm was employed to predict the optimal solution that balances shape memory performance and degradation performance, achieving a shape fixing ratio (Rf) of 93.5%, a shape recovery ratio (Rr) of 93.6%, and a degradation rate of 4.374%. Recycled and reprocessed PBST/PLA foam maintains over 95% of its shape memory performance. By integrating domain expertise with neural network, the knowledge guided neural network framework is applicable not only to foams, but can also be extended to performance prediction and optimization of a wider range of materials, including porous materials, gel materials, and fiber materials, as well as to more complex design scenarios. The knowledge-guided neural network framework represents a paradigm shift from traditional "trial and error" to inverse design, enabling the sustainable and intelligent development of biodegradable functional materials.