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RESEARCH PAPER · ACS Applied Materials & Interfaces · 2023

Visualization of Deep Convolutional Neural Networks to Investigate Porous Nanocomposites for Electromagnetic Interference Shielding

Meng Shi, Chang‐Ping Feng, Youlei Tu, Guang-Sheng Shi, Peiyao He, Yang Zhang, Jie Zhang, Jiang Li, Shaoyun Guo

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Visualization of Deep Convolutional Neural Networks to Investigate Porous Nanocomposites for Electromagnetic Interference ShieldingOriginalDeep learning-assisted morphological segmentation for effective particle area estimation and prediction of interfacial properties in polymer composites13Recent Trends in Polymeric Foams and Porous Structures for Electromagnetic Interference Shielding Applications41Electromagnetic interference shielding composite aerogels with asymmetric structures developed in aid of neural network22Machine learning applications in nanomaterials: Recent advances and future perspectives94Nano- and Microscale Design of Electrically Conductive Bacterial Cellulose/PEDOT Cryogels for Electromagnetic Interference Shielding1Deep-learning approach for developing bilayered electromagnetic interference shielding composite aerogels based on multimodal data fusion neural networks15Excellent absorption-dominant electromagnetic interference shielding performances of asymmetric gradient layered composite films exploited with assistance of machine learning15Integrating deep learning, fractal analysis to construct the relationship between microstructure and tensile strength of polypropylene foams1Design of ANF / MXene / SSG Sandwich Structure With Electromagnetic Shielding Performance and Impact Resistance1Photopolymerization 3D printing of structural electromagnetic wave absorbing materials: progress, challenges, and prospects3From prediction to understanding: A review of XAI applications and innovations in materials science8AI-powered open-source infrastructure for accelerating materials discovery and advanced manufacturing27Extreme electromagnetic wave absorption properties of graphene through machine learning3Machine Learning-Empowered Electromagnetic Wave Absorbing Materials: From Forward Prediction to Generative Inverse Design4Data-driven prediction and experimental validation of the shielding absorption performance of polyolefin-based EMI shielding composites0

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  1. Deep learning-assisted morphological segmentation for effective particle area estimation and prediction of interfacial properties in polymer composites

    Nanoscale · 2024-01-01

    Rasool Mohsenzadeh, Behzad Hashemi Soudmand, Amirhossein Najafi, Fawwaz Hazzazi, Mehdi Fattahi

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  2. Recent Trends in Polymeric Foams and Porous Structures for Electromagnetic Interference Shielding Applications

    Polymers · 2024-01-09

    Marcelo de Sousa Pais Antunes

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

    Composites Science and Technology · 2024-03-29

    Chenglei He, Lingjun Zeng, Bai Xue, Xingxing Zhang, Liya Yu, Lan Xie, Qiang Zheng

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  4. Machine learning applications in nanomaterials: Recent advances and future perspectives

    Chemical Engineering Journal · 2024-10-14

    Liang Yang, Hong Wang, Deying Leng, Shipeng Fang, Yanning Yang, Yanhai Du

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  5. Nano- and Microscale Design of Electrically Conductive Bacterial Cellulose/PEDOT Cryogels for Electromagnetic Interference Shielding

    Langmuir · 2025-02-19

    Shakiba Samsami, Majed Amini, Seyed Mohammad Amin Ojagh, Estatira Amirieh, Ayako Takagi, Theo G. M. van de Ven, Mohammad Arjmand, Orlando José Rojas, Kam Chiu Tam, Milad Kamkar

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

    Journal of Colloid and Interface Science · 2025-02-20

    Chenglei He, Liya Yu, Yun Jiang, Lan Xie, Xiaoping Mai, Peng Ai, Bai Xue

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  7. Excellent absorption-dominant electromagnetic interference shielding performances of asymmetric gradient layered composite films exploited with assistance of machine learning

    Journal of Colloid and Interface Science · 2025-05-20

    Lingjun Zeng, Yu Zhang, Xiaoping Mai, Peng Ai, Lan Xie, Bai Xue, Qiang Zheng

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  8. Integrating deep learning, fractal analysis to construct the relationship between microstructure and tensile strength of polypropylene foams

    Arabian Journal of Chemistry · 2025-09-15

    Qi Chen, Tianyu Xie, Zhao Yang, Zhipeng Yang, Anping Wang, Wei Gong

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  9. Design of ANF / MXene / SSG Sandwich Structure With Electromagnetic Shielding Performance and Impact Resistance

    Journal of Applied Polymer Science · 2026-01-06

    Yuanqi Gao, Jianbin Qin, Siying Li, Shuhuan Yun, Xianzhe Sheng, Zhenyu Xiong, Zhonglei Ma, Gefeng Shen, Kang Wang, Guangcheng Zhang

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  10. Photopolymerization 3D printing of structural electromagnetic wave absorbing materials: progress, challenges, and prospects

    Materials Today Chemistry · 2026-01-13

    Xinyi Zhang, Bin Yuan, Xianshun Wei, Wei Lu

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  11. From prediction to understanding: A review of XAI applications and innovations in materials science

    Computational Materials Science · 2026-02-12

    Fiorella Cravero, Ignacio Ponzoni, Mónica F. Díaz, Gustavo E. Vázquez

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  12. AI-powered open-source infrastructure for accelerating materials discovery and advanced manufacturing

    Communications Materials · 2026-02-17

    Mariangeles Salas, Anand Singh, Carlos Pignataro, Lokendra Pal

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  13. Extreme electromagnetic wave absorption properties of graphene through machine learning

    Carbon · 2026-05-16

    Zhiyu Du, Ye Yuan, Leyao Wang, Xiaohan Wang, Peng Kang, Lei Zheng, Yibin Li

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  14. Machine Learning-Empowered Electromagnetic Wave Absorbing Materials: From Forward Prediction to Generative Inverse Design

    Molecules · 2026-07-08

    Tongbaihui Qi, Jie Zhou

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  15. Data-driven prediction and experimental validation of the shielding absorption performance of polyolefin-based EMI shielding composites

    Journal of Alloys and Compounds · 2026-09-01

    Qi Chen, Yundan Du, Zhao Yang, Yongqiang Guo, Yali Zhang, Wei Gong

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