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July 10, 2026MoleculesOpen Access

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

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Authors

TQTongbaihui QiJZJie Zhou

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Overview

Review summarizes machine learning advancements in electromagnetic wave absorbers, highlighting design efficiency and future directions.

Key Points

  • This review aims to explore the integration of machine learning in designing electromagnetic wave absorbing materials.
  • Summarized recent advancements in machine-learning applications for electromagnetic wave absorption.
  • Discussed various machine learning models, including classical, deep, and generative models.
  • Evaluated challenges and future directions for implementing these technologies in design processes.
  • Machine learning enhances the design efficiency of absorbers by transitioning from trial-and-error to data-driven methods.
  • Highlighted the importance of data standardization and physics-guided learning in future advancements.
  • Identified ongoing challenges such as data quality and the need for engineering-scale validation.

Cite This Study

Qi et al. (2026) studied this question.

synapsesocial.com/papers/6a508d096eeac72a437a0cf8https://doi.org/10.3390/molecules31142408
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