arXiv:2607. 25145v1 Announce Type: cross Abstract: We implement an agentic AI workflow built around a large language model (LLM) agent for autonomous experiments with nitrogen-vacancy (NV) centers in diamond.
By Takuya Isogawa, Ryotaro Okabe, Nutdech Phadetsuwannukun, Mingda Li, Paola Cappellaro
HERA is an autonomous agent that performs cryogenic transport measurements on quantum devices using a curated knowledge base of protocols, software, and literature. In a blinded trial, HERA matched an expert’s conclusions on the stacking order of multilayer graphene devices, and ablation and replay studies demonstrated that the knowledge base improves measurement design and reasoning across models. When tasked with discovering superconductivity in a new material, HERA autonomously developed and executed the experiment after minimal expert input, illustrating its capability for sound experimental science.
By Zachary W. Gomez, Nathan Bittner, Jordan T. McCourt, Qingrui Cao, Sandesh S. Kalantre, Benjamin H. Alexander, Zhiren Zheng, Kenji Watanabe, Takashi Taniguchi, Aaron Sharpe
arXiv:2603. 13191v2 Announce Type: replace-cross Abstract: While large language models (LLMs) have transformed AI agents into proficient executors of computational materials science, performing a hundred simulations does not make a researcher.
By Haonan Huang
The article titled "The convergent laboratory: when AI reasoning, autonomous experiments, high performance and quantum computing reshape chemistry" discusses insights from the TPC26 conference, where leaders from academia, national laboratories, and industry examined how AI, autonomous agents, self-driving labs, high‑performance computing, and quantum computing converge to accelerate materials science discovery. It presents firsthand experiences from researchers at the forefront of these technologies and argues that their simultaneous maturation marks a tipping point for transformative advances and productive disruption in chemical sciences.
By Eliu Huerta, Xiaoyun Wang, Geetika Gupta, Edward H. Sargent, Cameron J. Owen, Victor Fung, Abhishek Mitra, Austin Cheng, Emma Bouchard, Shams Mehdi
The paper introduces QUANTA, an agentic AI framework designed for autonomous characterization of quantum materials using a Physical Property Measurement System. QUANTA integrates literature retrieval, experimental planning, troubleshooting, and data interpretation, with a deterministic control layer ensuring safe instrument operation. Demonstrations include transport measurements on the Weyl semimetal TaP and nonlinear response studies in the magnetic material Co2MnGa, showcasing QUANTA’s generalizable, evidence-driven, closed-loop capabilities.
By Eunbi Rha, Abhijatmedhi Chotrattanapituk, Mouyang Cheng, Chung-tao Chou, Shenyong Ye, Ashley S. Dale, Long Ju, Luqiao Liu, Mingda Li
The paper introduces AI Theorist, a system of AI agents that autonomously discovers physical models by generating hypotheses, performing first‑principles calculations, and refining them based on evidence. Applied to the quantum material α‑RuCl₃, the system interprets optical and photocurrent data, revealing distinct excitonic states with different optical selection rules and spatial distributions. This marks the first demonstration of an AI system autonomously developing a physical model to explain previously unpublished experimental observations in a quantum material using first‑principles and many‑body calculations.
By Hongjian Zhou, Xianfan Nie, Sean Wu, Tarun Patel, Jinge Wu, Andrew Liu, Adam Wei Tsen, David A. Clifton