Data For an AI-driven robotic system for two-dimensional hetero-assemblies

Nanomaterials stacked on demand, such as rotationally assembled two-dimensional (2D) van der Waals layered compounds, provide a versatile platform for quantum simulation and the exploration of exotic electronic phases. However, such nanoassemblies remain largely confined to inefficient, manually operated processes at present, limiting their potential for probing emergent physical phenomena. There is a pressing need in the field for high-precision, automated assembly 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 the dry transfer of exfoliated 2D materials. This system further employs metadata generated from each automated stacking procedure to perform reinforcement learning, thereby continuously bettering its performance. As a concrete demonstration, we fabricate twisted bilayer graphene, 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, in which the integrated data mining and artificial intelligence could accelerate the discovery of novel physical phenomena.