Electromagnetic interference shielding composite aerogels with asymmetric structures developed in aid of neural network

Along with the increasingly serious electromagnetic interference (EMI) pollution, it is in urgent need of exploiting high-performance EMI shielding materials. However, the conventional experimental methods always require a series of tedious preparation and characterization processes, resulting in the long period and high cost. Herein, a fully connected neural network (FCNN) was applied to aid in exploiting silver nanowires/waterborne polyurethan/multi-walled carbon nanotubes (AgNWs/WPU/MWCNTs) EMI shielding aerogels with asymmetric structures to optimize new material evolution and reduce experiment burden. Firstly, the special AgNWs/WPU/MWCNTs aerogels were fabricated with various MWCNTs content (0 wt.%, 2 wt.%, and 4 wt.%) and AgNWs mass (10 mg, 20 mg, 30 mg, and 40 mg) via a combination of directional freezing and spraying procedures, leading to 12 distinct samples. AgNWs/WPU/MWCNTs aerogels hold excellent EMI shielding properties and absorption-dominant shielding mechanism. The experimental EMI shielding effectiveness (SE) data at different frequency were leveraged to establish, train, and test the FCNN model based on Adam optimizer (Adam-FCNN). It is worth noting that Adam-FCNN model predictions are in good accordance with the EMI SE of the physically measured samples, with the lowest root mean square error RMSE of 0.7899, the highest correlation coefficient (R) of 0.9895, and excellent computational efficiency and reliability. Moreover, Adam-FCNN can accurately prognosticate EMI SE of unobtained AgNWs/WPU/MWCNTs aerogels in mechanics-intuitions to reduce repeated preparation and characterizing conditions. Thereby, Adam-FCNN soft model approach opens significant potential for alleviating the experimental burden, accelerating the discovery of innovative materials, and exploring the complex mechanism.