多功能双连续多尺度结构的数据驱动反向设计
Lili Wang1,2, Jingxuan Feng1, Xiaoya Zhai3
1School of Mathematical Sciences, University of Science and Technology of China, Hefei, Anhui, China.
Nature communications
|January 8, 2026
概括
本研究介绍了用于设计复杂结构的大范围,边界相同,双连续和开放细胞微结构 (L-BOM) 数据集. 这些人工智能生成的数据集能够快速,数据驱动的反向设计具有可调节性质的多功能材料.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机建模 计算建模
- 添加剂制造 添加剂制造 添加剂制造
背景情况:
- 双连续的多尺度结构拥有独特的相互连接网络,提供卓越的性能.
- 由于缺乏分析模型和优化中的计算复杂性,设计这些结构具有挑战性.
- 现有的方法通常需要后处理,以确保结构完整性和连接性.
研究的目的:
- 呈现新的3D大范围,边界相同,双连续和开放细胞微结构 (L-BOM) 数据集.
- 为了实现多功能双连续结构的快速,数据驱动的反向设计.
- 克服复杂微观结构当前设计方法的局限性.
主要方法:
- 开发生成型人工智能模型.
- 积极学习技术的应用用于数据集生成.
- 创建具有相同边界和广泛财产覆盖的数据集.
主要成果:
- 生成的L-BOM数据集,具有具有保证连接性的双连续开放细胞架构.
- 证明了广泛的财产覆盖面,用于广泛的性能探索.
- 成功地利用数据集进行反向设计,而不需要用于连接的后处理.
结论:
- L-BOM 数据集显著提升了双连续多层结构的设计能力.
- 这些数据集促进了对多功能材料的大型物业空间的探索.
- 潜在的应用包括骨植入物的设计,椅子部件,以及具有可调节弹性和透性的材料.
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