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RESEARCH PAPER · Composites Science and Technology · 2022

Machine learning to optimize nanocomposite materials for electromagnetic interference shielding

Meng Shi, Chang‐Ping Feng, Jiang Li, Shaoyun Guo

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Machine learning to optimize nanocomposite materials for electromagnetic interference shieldingOriginalAn Interpretable Machine Learning Model to Guide the Design of Organic Phosphorus-Containing Flame Retardants for Improving the Limiting Oxygen Index of Epoxy Resins0Epoxy Coatings Containing Modified Graphene for Electromagnetic Shielding17Machine learning accelerates the materials discovery142Machine learning-guided design of organic phosphorus-containing flame retardants to improve the limiting oxygen index of epoxy resins76Prediction and Evaluation of Indirect Carbon Emission from Electrical Consumption in Multiple Full-Scale Wastewater Treatment Plants via Automated Machine Learning-Based Analysis53Machine learning for the regulation strategy and mechanism of the integrated growth of carbon nanotube arrays9Machine Learning Techniques for the Design and Optimization of Polymer Composites: A Review20The effects of nano-additives on the mechanical, impact, vibration, and buckling/post-buckling properties of composites: A review105Two-layer structural method: An efficient strategy inspired by machine learning for nanocomposites with magnetic particles for electromagnetic interference shielding23A predictive model for the relationship between processing conditions and properties of thermoplastic vulcanizates (TPVs) via machine learning32Accelerated feasible screening of flame-retardant polymeric composites using data-driven multi-objective optimization13Vibration optimization of cantilevered bistable composite shells based on machine learning20A High-Generalizability Machine Learning Framework for Analyzing the Homogenized Properties of Short Fiber-Reinforced Polymer Composites31Advancements in Microwave Absorption Motivated by Interdisciplinary Research310Ensemble machine learning for predicting the homogenized elastic properties of unidirectional composites: A SHAP-based interpretability analysis32AI- and ML-based Models for Predicting Remaining Useful Life (RUL) of Nanocomposites and Reinforced Laminated Structures7A deep transfer learning approach to construct the allowable load space of notched composite laminates40Recent Trends in Polymeric Foams and Porous Structures for Electromagnetic Interference Shielding Applications41Recent progress in smart electromagnetic interference shielding materials227Advances in materials informatics: a review42Investigation on surface metallization of epoxy resin and its electromagnetic interference shielding performance1Optimization of (Ba1−xCax)(Ti0.9Sn0.1)O3 ceramics in X-band using Machine Learning20The chemistry, properties and performance of flame-retardant rubber composites: Collecting, analyzing, categorizing, machine learning modeling, and visualizing37Electromagnetic interference shielding composite aerogels with asymmetric structures developed in aid of neural network22Improvement the classification of a nanocomposite using nanoparticules based on a meta-analysis study, Recurrent Neural Network and Recurrent Neural Network Monte-Carlo algorithms1Prevention of Leakage in Machine Learning Prediction for Polymer Composite Properties16Polymer based composites for electromagnetic interference (EMI) shielding: The role of magnetic fillers in effective attenuation of microwaves, a review75Machine learning to predict effective conductivity of composite materials for shielding applications0A Machine Learning Aided Yield Prediction Model for the Preparation of Cellulose Nanocrystals19Improving the classification of a nanocomposite using nanoparticles based on a meta-analysis study, recurrent neural network and recurrent neural network Monte-Carlo algorithms11Effective Electromagnetic Properties of Composite Material Computed From Neural Network Approach1Multifunctional strain sensor with adjustable conductive network for wearable applications1Data‐Driven Materials Research and Development for Functional Coatings120Research on high-entropy spinel microwave absorption materials: Exploration of machine learning and experimental integration20Research progress of carbon-based materials in intumescent fire-retardant coatings: A review15Usage of Machine Learning Techniques to Classify and Predict the Performance of Force Sensing Resistors2Boosting machine learning algorithms for predicting the macroscopic material behavior of continuous fiber reinforced composite26Optimizing carbon source addition to control surplus sludge yield via machine learning-based interpretable ensemble model12Application of Machine Learning to Identify Key Features Affecting Conductivity in CNT/ PEDOT:PSS Nanocomposites0Enhancing Copper Interconnect Reliability Based on Machine Learning: Electromigration Lifetime Prediction Via an -Interpretable Qpso-Xgboost Hybrid Model0Exploring the nonlinear conductive properties of polymer/graphene composites at the molecular level: a machine learning approach4Prediction of the compressive strength and carpet plot for cross-material CFRP laminate based on deep transfer learning8SCS-Net: A DNN-based electromagnetic shielding effectiveness analysis method for slotted composite structures4Machine-learning assisted novel insulation layer stripping technology for upgrading the transparent EMI shielding materials27Deep-learning approach for developing bilayered electromagnetic interference shielding composite aerogels based on multimodal data fusion neural networks15Application of machine learning to identify key features affecting conductivity in CNT/ PEDOT:PSS nanocomposites2Advancements in CNT research: Integrating machine learning with microscopic simulations, macroscopic techniques, and application of performance prediction and functional optimization6Performance and mechanism of a graphene assembled film-based flexible sensor for strain monitoring of civil infrastructures14Advancing polymer nanocomposites through mechanochemical approaches25Selection of oxygen carrier for chemical looping combustion of natural gas and syngas fuels – A machine learning approach8Perspectives on absorption-dominant electromagnetic interference shielding materials with MXene and carbon-based polymer composites36Machine learning-driven constructing relationship between processing and mechanical properties of TPV: A view of one stacking model4Recent innovations in EMI shielding materials for stealth technology18Research advancements in machine learning-assisted design of reinforced composite radiation shields8Deep Artificial Neural Network Modeling of the Ablation Performance of Ceramic Matrix Composites in the Hydrogen Torch Test2A review of artificial intelligence (AI)-based applications to nanocomposites17MXene‐Based Electromagnetic Interference Shielding Materials: A Leap From Fundamental Research to Intelligent Customization26Investigation for the influence of Sm2O3 and CeO2 nanoparticles on the microstructure and electrochemical behavior of epoxy and prediction of mechanical characterizations of adhesive joining of CFPEEK via machine learning19Electrospinning for electromagnetic interference shielding: Principles, challenges, and future directions25Ceramic-based electromagnetic interference shielding materials: Mechanisms, optimization strategies, and pathways to next-generation applications26Geometric Optimization of Passive High‐Frequency Electromagnetic Shielding Structures Based on Finite Element Analysis and Deep Learning0Harnessing Machine Learning for EMI Shielding Materials: State-of-the-Art Advances and Prospects Ahead0Artificial Intelligence-Powered prediction and optimization of compressive strength in lightweight hemp-based blocks for sustainable construction4Asymmetric multistage composite foams for high-efficient absorption-dominant electromagnetic interference shielding exploited with aid of machine learning6Machine Learning‐Accelerated Discovery of High‐Performance Insulating Electromagnetic Interference Shielding Composites for Integrated Electronics11Interpretable high-throughput machine learning-based screening of high-thermal-conductivity composites with experimental validation6MOPSO-Based Data Augmentation and Hardness-Assisted Interpretable Machine Learning for Accurate Prediction of Mechanical Properties of Mg Alloys0Machine-learning for capacitive energy storage polymer dielectrics: opportunities and challenges9Ensemble machine learning framework for predicting and optimizing electromagnetic shielding of Al 6061 hybrid metal matrix composites0Deciphering peroxydisulfate vs. peroxymonosulfate activation by biochar catalysts via explainable machine learning0Urban thermal evolution and baseline mapping of summer daytime land surface temperature in Chongqing, China4Machine Learning-Empowered Electromagnetic Wave Absorbing Materials: From Forward Prediction to Generative Inverse Design4Artificial intelligence and machine learning in composite materials: A comprehensive literature review and bibliometric analysis3A heterogeneous ensemble AI framework for predicting dissolved sulfide in urban sewer networks1Machine learning-assisted development of asymmetric melamine foam composites featuring a dendritic copper sulfide conductive skeleton and a planar conductive coating0Rise of asymmetric structures for absorption-dominant electromagnetic interference shielding0Data-driven prediction and experimental validation of the shielding absorption performance of polyolefin-based EMI shielding composites0Additive Manufacturing for Sustainable Electromagnetic Interference Shielding: Materials Discovery, Structural Optimization, and Machine Learning0Recent advances in perovskite oxide-based materials for electromagnetic interference shielding and microwave absorption03D architected carbon-based electromagnetic wave absorbers: A perspective on design and manufacturing strategies0

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  2. Epoxy Coatings Containing Modified Graphene for Electromagnetic Shielding

    Polymers · 2022-06-20

    Marius Gabriel Bontaș, Aurel Diacon, Ioan Călinescu, Mădălina Ioana Necolau, Adrian M. Dinescu, Gabriela Toader, Raluca Elena Ginghină, Alexandru-Mădălin Vizitiu, Valentin Velicu, Petru Palade, Marcel Istrate, Edina Rusen

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  3. Machine learning accelerates the materials discovery

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  4. Machine learning-guided design of organic phosphorus-containing flame retardants to improve the limiting oxygen index of epoxy resins

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  5. Prediction and Evaluation of Indirect Carbon Emission from Electrical Consumption in Multiple Full-Scale Wastewater Treatment Plants via Automated Machine Learning-Based Analysis

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  6. Machine learning for the regulation strategy and mechanism of the integrated growth of carbon nanotube arrays

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  7. Machine Learning Techniques for the Design and Optimization of Polymer Composites: A Review

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  8. The effects of nano-additives on the mechanical, impact, vibration, and buckling/post-buckling properties of composites: A review

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  9. Two-layer structural method: An efficient strategy inspired by machine learning for nanocomposites with magnetic particles for electromagnetic interference shielding

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  10. A predictive model for the relationship between processing conditions and properties of thermoplastic vulcanizates (TPVs) via machine learning

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  11. Accelerated feasible screening of flame-retardant polymeric composites using data-driven multi-objective optimization

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  12. Vibration optimization of cantilevered bistable composite shells based on machine learning

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  13. A High-Generalizability Machine Learning Framework for Analyzing the Homogenized Properties of Short Fiber-Reinforced Polymer Composites

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  14. Advancements in Microwave Absorption Motivated by Interdisciplinary Research

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  15. Ensemble machine learning for predicting the homogenized elastic properties of unidirectional composites: A SHAP-based interpretability analysis

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  16. AI- and ML-based Models for Predicting Remaining Useful Life (RUL) of Nanocomposites and Reinforced Laminated Structures

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  17. A deep transfer learning approach to construct the allowable load space of notched composite laminates

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

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  19. Recent progress in smart electromagnetic interference shielding materials

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  20. Advances in materials informatics: a review

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  21. Investigation on surface metallization of epoxy resin and its electromagnetic interference shielding performance

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  22. Optimization of (Ba1−xCax)(Ti0.9Sn0.1)O3 ceramics in X-band using Machine Learning

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

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  38. Optimizing carbon source addition to control surplus sludge yield via machine learning-based interpretable ensemble model

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

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  46. Application of machine learning to identify key features affecting conductivity in CNT/ PEDOT:PSS nanocomposites

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  49. Advancing polymer nanocomposites through mechanochemical approaches

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