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<p>Energetic materials typically lack precise control over energy release rates once ignited. Inverse design of their structures offers a promising solution by enabling regulation of burning surface evolution and energy release profiles, facilitating customizable combustion. Such a capability could significantly enhance energy management in aerospace and chemical industrial systems. While both optimization-driven and data-driven approaches have demonstrated their potential, their optimality, time efficiency, and feasibility cannot be guaranteed simultaneously. Here, we present a dual-model approach combining a deep eikonal auto-decoder and a denoising diffusion probabilistic model to generate novel energetic material structures while accurately representing their entire burning surface evolution. A key to success is a optimization-driven fine-tuning technique that substantially improves the optimality while maintaining high volume loading efficiency and other critical constraints. This combination of optimization-driven and data-driven approaches opens a pathway toward fully customizable combustion of energetic materials, capable of precisely matching complex target performance curves.</p>

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Generative AI empowered inverse design of 3D energetic material structures for customizable combustion

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月22日

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  • Wentao Li,
  • Yiyi Zhang,
  • Hanyun Zheng,
  • Yunqin He,
  • Guozhu Liang

摘要

Energetic materials typically lack precise control over energy release rates once ignited. Inverse design of their structures offers a promising solution by enabling regulation of burning surface evolution and energy release profiles, facilitating customizable combustion. Such a capability could significantly enhance energy management in aerospace and chemical industrial systems. While both optimization-driven and data-driven approaches have demonstrated their potential, their optimality, time efficiency, and feasibility cannot be guaranteed simultaneously. Here, we present a dual-model approach combining a deep eikonal auto-decoder and a denoising diffusion probabilistic model to generate novel energetic material structures while accurately representing their entire burning surface evolution. A key to success is a optimization-driven fine-tuning technique that substantially improves the optimality while maintaining high volume loading efficiency and other critical constraints. This combination of optimization-driven and data-driven approaches opens a pathway toward fully customizable combustion of energetic materials, capable of precisely matching complex target performance curves.