近日，美国雪城大学的Zhao Qin及其研究小组取得一项新进展。经过不懈努力，他们利用生成式人工智能实现双连续复合材料的数据高效力学设计。相关研究成果已于2024年1月9日在国际知名学术期刊《理论与应用力学快报》上发表。

该研究团队专注于双连续复合结构的快速生成与加载时的应力分布问题。研究人员发现，通过微调低秩自适应模型，生成式人工智能可以利用少量的输入数据进行训练，进而生成具有特定力学性能的合成复合结构及其对应的von Mises应力分布。研究结果表明，这种技术能够便捷地生成大量复合材料设计方案，并从中提取出有用的力学信息。

这些信息可以整合到一个模型中，用以预测材料的刚度、断裂性能和鲁棒性等关键力学特性。而这些特性的评估通常需要依赖于多个不同的实验或模拟测试。本研究为改进复合材料设计提供了有价值的见解，不仅扩大了设计空间，还实现了复合材料设计的自动筛选，旨在进一步提升材料的力学性能。

据悉，材料相的分布对复合材料的力学性能起着决定性的作用。对于高度有序的材料分布，可以通过有限案例研究来建立完整的结构-力学关系。然而，对于复杂的无序分布，这种关系很难被揭示，导致材料结构设计无法满足特定的力学要求。随着人工智能(AI)算法在材料设计领域的显著发展，人们能够检测到隐藏的结构力学相关性，这对于设计具有复杂结构的复合材料至关重要。这些工具如何帮助复合材料设计是很有趣的。

附：英文原文

Title: Towards Data-efficient Mechanical Design of Bicontinuous Composites Using Generative AI

Author: Milad Masrouri, Zhao Qin

Issue&Volume: 2024-01-09

Abstract: The distribution of material phases is crucial to determine the composite’s mechanical property. While the full structure-mechanics relationship of highly ordered material distributions can be studied with finite number of cases, this relationship is difficult to be revealed for complex irregular distributions, preventing design of such material structures to meet certain mechanical requirements. The noticeable developments of artificial intelligence (AI) algorithms in material design enables to detect the hidden structuremechanics correlations which is essential for designing composite of complex structures. It is intriguing how these tools can assist composite design. Here, we focus on the rapid generation of bicontinuous composite structures together with the stress distribution in loading. We find that generative AI, enabled through fine-tuned Low Rank Adaptation models, can be trained with a few inputs to generate both synthetic composite structures and the corresponding von Mises stress distribution. The results show that this technique is convenient in generating massive composites designs with useful mechanical information that dictate stiffness, fracture and robustness of the material with one model, and such has to be done by several different experimental or simulation tests. This research offers valuable insights for the improvement of composite design with the goal of expanding the design space and automatic screening of composite designs for improved mechanical functions.

DOI: 10.1016/j.taml.2024.100492

Source: http://taml.cstam.org.cn/article/doi/10.1016/j.taml.2024.100492