Inverse designing metamaterials with programmable nonlinear functional responses in graph space
<|reference_start|>Inverse designing metamaterials with programmable nonlinear functional responses in graph space: Material responses to static and dynamic stimuli, represented as nonlinear curves, are design targets for engineering functionalities like structural support, impact protection, and acoustic and photonic bandgaps. Three-dimensional metamaterials offer significant tunability due to their internal structure, yet existing methods struggle to capture their complex behavior-to-structure relationships. We present GraphMetaMat, a graph-based framework capable of designing three-dimensional metamaterials with programmable responses and arbitrary manufacturing constraints. Integrating graph networks, physics biases, reinforcement learning, and tree search, GraphMetaMat can target stress-strain curves spanning four orders of magnitude and complex behaviors, as well as viscoelastic transmission responses with varying attenuation gaps. GraphMetaMat can create cushioning materials for protective equipment and vibration-damping panels for electric vehicles, outperforming commercial materials, and enabling the automatic design of materials with on-demand functionalities.<|reference_end|>
@article{maurizi2024inverse,
    title={Inverse designing metamaterials with programmable nonlinear functional
  responses in graph space},
    author={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},
    journal={arXiv preprint arXiv:2408.06300},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06300},
    primaryClass={cond-mat.mtrl-sci cs.LG}
}