Qumus: Realization of An Embodied AI Quantum Material Experimentalist

TL;DR

Qumus is an embodied AI robotic system capable of autonomously conducting quantum materials experiments, including creating graphene and nanodevices, demonstrating autonomous learning and error correction in real-world scientific discovery.

Contribution

First autonomous embodied AI system for quantum materials experimentation, integrating high-level reasoning, multimodal processing, and real-time physical execution.

Findings

Achieved AI-creation of graphene.

Fabricated complex nanodevices including atomically thin FETs.

Demonstrated autonomous error correction and closed-loop experimentation.

Abstract

While modern Large Language Models (LLMs) and agentic artificial intelligence (AI) have demonstrated transformative capabilities in digital domains, the realization of embodied AI capable of real-world scientific discovery remains a difficult frontier. The advancements are hindered by the inherent complexity of integrating high-level reasoning, multimodal information processing and real-time physical execution. Here we introduce Qumus, the first AI quantum materials experimentalist. Physically embodied within a robotic mini-laboratory, Qumus is an intelligent, multimodal, and multi-agent system designed for the creation and nano-processing of atomically thin two-dimensional (2D) materials and stacked van der Waals (vdW) structures. Qumus autonomously navigates the full scientific cycle, from hypothesis generation and protocol planning to multi-step experimental execution, result analysis and reporting, acting as an experimentalist. Markedly, the system has achieved, for the first time, the AI-creation of graphene, as well as the first AI-fabrication of complex nanodevices including atomically thin field-effect transistors via vdW stacking. Qumus excels at these tasks by demonstrating autonomous error correction and closed-loop experimentation. Our results establish a generalizable framework for self-improving embodied AI systems that learn directly from the quantum world, opening a pathway toward accelerated discovery in quantum materials, electronics and beyond.