Citation: Xiaochuang Di, Mushan Yuan, Junyu Lu, Shenquan Yang, Cuiqing Zhou, Yang Chen, Huawei Zou. Navigating the continuous parameter space for ultra-broadband microwave absorption: a sequence-aware deep learning and evolutionary optimization approach[J]. Acta Physico-Chimica Sinica, ;2026, 42(9): 100270. doi: 10.1016/j.actphy.2026.100270 shu

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

  • Corresponding author: Yang Chen, cy3262276@163.com Huawei Zou, hwzou@163.com
  • Received Date: 13 January 2026
    Revised Date: 12 February 2026
    Accepted Date: 27 February 2026

  • 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.
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