Acta Phys. -Chim. Sin. ›› 2026, Vol. 42 ›› Issue (9): 100270.doi: 10.1016/j.actphy.2026.100270

• ARTICLE • Previous Articles     Next Articles

Navigating the continuous parameter space for ultra-broadband microwave absorption: a sequence-aware deep learning and evolutionary optimization approach

Xiaochuang Di, Mushan Yuan, Junyu Lu, Shenquan Yang, Cuiqing Zhou, Yang Chen*(), Huawei Zou*()   

  1. The State Key Lab of Polymer Materials Engineering, Polymer Research Institution, Sichuan University, Chengdu 610065, Sichuan Province, China
  • Received:2026-01-13 Revised:2026-02-12 Accepted:2026-02-27 Published:2026-07-03
  • Contact: Email: cy3262276@163.com (Yang Chen)hwzou@163.com (Huawei Zou)

Abstract:

The rapid proliferation of electromagnetic (EM) pollution necessitates the urgent development of high-performance microwave absorption (MA) materials with ultra-broadband capabilities. However, conventional trial-and-error design paradigms are constrained by the path-dependent nature of the fabrication process, where the specific impregnation history strictly governs the final gradient distribution and impedance matching. To address this, this study proposes a sequence-aware inverse design framework that integrates a Long Short-Term Memory (LSTM) neural network with a Genetic Algorithm (GA). Leveraging a process-property database derived from multi-step impregnated polyurethane/carbon nanotube (PU/CNT) foams, a high-fidelity LSTM surrogate model is developed to decode the complex temporal dependencies within the impregnation history and accurately predict frequency-dependent complex permittivity. Subsequently, the GA utilizes this predictive model to navigate the design space, identifying an optimal impregnation pathway that yields a precise three-layer gradient configuration. The resulting optimized foam, characterized by a rational stepwise increase in dielectric loss, achieves an exceptional average reflection loss (RL) of −24.2 dB and an ultra-wide effective absorption bandwidth (EAB) covering the full 2–18 GHz range. This work demonstrates the efficacy of history-based data-driven strategies in accelerating material discovery, offering a scalable paradigm for the intelligent design of advanced functional composites.

Key words: Microwave absorption, Inverse design, Long short-term memory (LSTM) networks, Genetic algorithm, Gradient structures