Deep-learning approach for developing bilayered electromagnetic interference shielding composite aerogels based on multimodal data fusion neural networks.

Journal: Journal of colloid and interface science
Published Date: Feb 20, 2025

Abstract

A non-experimental approach to developing high-performance EMI shielding materials is urgently needed to reduce costs and manpower. In this investigation, a multimodal data fusion neural network model is proposed to predict the EMI shielding performances of silver-modified four-pronged zinc oxide/waterborne polyurethane/barium ferrite (Ag@F-ZnO/WPU/BF) aerogels. First, 16 Ag@F-ZnO/WPU/BF samples with varying Ag@F-ZnO and BF contents were successfully prepared using the pre-casting and directional freezing techniques. The experimental results demonstrate that these aerogels perform well in terms of averaged EMI shielding effectiveness (SE) up to 78.6 dB and absorption coefficient as high as 0.96. On the basis of composite ingredients and microstructural images, the established multimodal neural network model can effectively predict the EMI shielding performances of Ag@F-ZnO/WPU/BF aerogels. Notably, the multimodal model of fully connected neural network (FCNN) and residual neural network (ResNet) utilizing GatedFusion method yields the best root mean squared error (RMSE) and mean absolute error (MAE) values of 0.7626 and 0.4918, respectively, and correlation coefficient (R) of 0.9885. In addition, this multimodal model successfully predicts the EMI performances of four new aerogels with an average error of less than 5 %, demonstrating its strong generalization capability. The accuracy and efficiency of material property prediction based on multimodal neural network model are largely improved by integrating multiple data sources, offering new possibility for reducing experimental burdens, accelerating the development of new materials, and gaining a deeper understanding of material mechanisms.

Authors

  • Chenglei He
    College of Mechanical Engineering, Guizhou University, Guiyang 550025, China.
  • Liya Yu
    College of Mechanical Engineering, Guizhou University, Guiyang 550025, China. Electronic address: lyyu@gzu.edu.cn.
  • Yun Jiang
  • Lan Xie
    Department of Polymer Materials and Engineering, College of Materials and Metallurgy, Guizhou University, Guiyang 550025, China; State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China; National Engineering Research Center for Compounding and Modification of Polymer Materials, National and Local Joint Engineering Research Center for Functional Polymer Membrane Materials and Membrane Processes, Guiyang 550014, China.
  • Xiaoping Mai
    Department of Polymer Materials and Engineering, College of Materials and Metallurgy, Guizhou University, Guiyang 550025, China.
  • Peng Ai
    College of Materials Science and Engineering, Liaoning Technical University, Fuxin, Liaoning 123000, China.
  • Bai Xue
    Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China.

Keywords

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