Nanomaterials stacked on-demand, such as rotationally assembled two-dimensional (2D) van der Waals (vdW) layered compounds, provides a versatile platform for quantum simulation and the exploration of exotic electronic phases. Currently, however, such nanoassemblies remain largely confined to inefficiency, manually operated process, limiting their potential for probing emergent physical phenomena. There is a pressing need in the field for high-precision, automated assembling techniques, especially for the scalable fabrication of 2D twistronic heterostructures. Here, we present an intelligent automation system dedicated to the fabrication of van der Waals stacks, following the state-of-the-art protocol for dry transfer of exfoliated 2D materials. The system further employs metadata generated from each automated stacking procedure to perform reinforcement learning, thereby continuously bettering its performances. As a concrete demonstration, we fabricate twisted bilayer graphene (TBLG) -- known for its challenging preparation -- and exhibit its unconventional superconductivity near the magic angle. Our work may pave the way for high-throughput fabrication of low-dimensional nanomaterials including twistronic heterostructures, where integrating data mining and artificial intelligence can accelerate the discovery of novel physical phenomena.
@article{arxiv.2605.20420,
title = {An AI-driven robotic system for two-dimensional hetero-assemblies},
author = {Xiaoxi Li and Jinkun He and Haojie Liu and Xipeng Liu and Zewen Wu and Jing Li and Kai Zhao and Shan Li and Xingdan Sun and Xiaoxue Fan and Zhiren Xiong and Xingguang Wu and Xuanzhe Sha and Zhili Lin and Caixia Yang and Luosha Han and Jie Xu and Woye Pei and Kaining Yang and Jing Zhang and Xiaolong Feng and Tongyao Zhang and Zhu Liang and Kenji Watanabe and Takashi Taniguchi and Ming Tian and Neng Wan and Jing Zhang and Jianming Lu and Wenjing Hong and Zheng Vitto Han},
journal= {arXiv preprint arXiv:2605.20420},
year = {2026}
}