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<p>Bicontinuous multiscale structures, commonly observed in nature, comprise two interpenetrating networks that are solid and void phases forming a continuous and interconnected system. These unique architectures exhibit superior multi-physical performances and multifunctionalities; however, their design has been limited by the lack of analytical expressions and the computational challenges in multiscale optimization. This study presents a 3D <b>L</b>arge-range, <b>B</b>oundary-identical, <b>B</b>icontinuous, and <b>O</b>pen-cell <b>M</b>icrostructure (L-BOM) datasets for the fast data-driven inverse design of multifunctional bicontinuous multiscale structures. Each dataset features identical boundaries, bicontinuous open-cell structures, and broad property coverage for performance exploration. These properties are satisfied by active learning techniques developed with a generative artificial intelligence model. The datasets hold significant promise for advancing the design of bicontinuous multiscale structures with a large-range property space, without additional post-processing to ensure the connectivity. This work further demonstrates the potential of the datasets in devising bone implants, chair components, and multifunctional materials with tunable elasticity and permeability.</p>

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Data-driven inverse design of multifunctional bicontinuous multiscale structures

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月8日

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  • Lili Wang,
  • Jingxuan Feng,
  • Xiaoya Zhai,
  • Jiacheng Han,
  • Kai Chen,
  • Winston Wai Shing Ma,
  • Ligang Liu,
  • Xiao-Ming Fu

摘要

Bicontinuous multiscale structures, commonly observed in nature, comprise two interpenetrating networks that are solid and void phases forming a continuous and interconnected system. These unique architectures exhibit superior multi-physical performances and multifunctionalities; however, their design has been limited by the lack of analytical expressions and the computational challenges in multiscale optimization. This study presents a 3D Large-range, Boundary-identical, Bicontinuous, and Open-cell Microstructure (L-BOM) datasets for the fast data-driven inverse design of multifunctional bicontinuous multiscale structures. Each dataset features identical boundaries, bicontinuous open-cell structures, and broad property coverage for performance exploration. These properties are satisfied by active learning techniques developed with a generative artificial intelligence model. The datasets hold significant promise for advancing the design of bicontinuous multiscale structures with a large-range property space, without additional post-processing to ensure the connectivity. This work further demonstrates the potential of the datasets in devising bone implants, chair components, and multifunctional materials with tunable elasticity and permeability.