2025-07-22 カリフォルニア大学バークレー校（UCB）
GraphMetaMat, an inverse design framework, enables users to create metamaterial designs, represented as graphs, entirely from scratch based on custom inputs. Its AI system then iteratively adds graph nodes and edges to define the material’s geometry and topology and integrates manufacturing and defect constraints. (Illustration courtesy of the researchers)
＜関連情報＞
- https://engineering.berkeley.edu/news/2025/07/a-smarter-approach-to-designing-metamaterials/
- https://www.nature.com/articles/s42256-025-01067-x
グラフ空間におけるプログラム可能な非線形応答と幾何学的制約を持つメタマテリアルの設計 Designing metamaterials with programmable nonlinear responses and geometric constraints in graph space
Marco Maurizi,Derek Xu,Yu-Tong Wang,Desheng Yao,David Hahn,Mourad Oudich,Anish Satpati,Mathieu Bauchy,Wei Wang,Yizhou Sun,Yun Jing & Xiaoyu Rayne Zheng
Nature Machine Intelligence Published:22 July 2025
DOI:https://doi.org/10.1038/s42256-025-01067-x
Abstract
Advances in data-driven design and additive manufacturing have substantially accelerated the development of truss metamaterials—three-dimensional truss networks—offering exceptional mechanical properties at a fraction of the weight of conventional solids. While existing design approaches can generate metamaterials with target linear properties, such as elasticity, they struggle to capture complex nonlinear behaviours and to incorporate geometric and manufacturing constraints—including defects—crucial for engineering applications. Here we present GraphMetaMat, an autoregressive graph-based framework capable of designing three-dimensional truss metamaterials with programmable nonlinear responses, originating from hard-to-capture physics such as buckling, frictional contact and wave propagation, along with arbitrary geometric constraints and defect tolerance. Integrating graph neural networks, physics biases, imitation learning, reinforcement learning and tree search, we show that GraphMetaMat can target stress–strain curves across four orders of magnitude and vibration transmission responses with varying attenuation gaps, unattainable by previous methods. We further demonstrate the use of GraphMetaMat for the inverse design of novel material topologies with tailorable high-energy absorption and vibration damping that outperform existing polymeric foams and phononic crystals, potentially suitable for protective equipment and electric vehicles. This work sets the stage for the automatic design of manufacturable, defect-tolerant materials with on-demand functionalities.
