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
arXiv:2601. 13508v4 Announce Type: replace-cross Abstract: Autonomous agents are beginning to transform scientific research from tool-assisted workflows toward self-sustaining discovery processes.
By Honghao Chen, Jiangjie Qiu, Yi Shen Tew, Xiaonan Wang
The article introduces the concept of agentic programs—scientific software that blends deterministic algorithms with bounded large‑language‑model (LLM) judgment, task‑specific verification, episodic maturation, and full delegation in production. It argues that recent LLM‑based agents enable this new form of computational materials science software. The authors illustrate the idea with DeMARS, an agentic program designed to build atomistic models from experimentally measured disordered crystal structures.
By Yunsung Lim, Haekwan Jeon, Jaesun Kim, Jisu Kim, Seungwu Han
arXiv:2608. 14407v1 Announce Type: new Abstract: We present a survey of the past and future of AI Scientists: machines capable of automating science.
By Ross D. King
arXiv:2608. 07454v1 Announce Type: cross Abstract: The total synthesis of a complex molecule is among the most demanding intellectual and experimental feats in chemistry: a chemist must plan many steps ahead for how to assemble simple building blocks into an intricate target, devise backup strategies, and anticipate procedural challenges.
By Daniel Armstrong, Xuan-Vu Nguyen, Octavian Susanu, Gabriel Gibberd, Th\'eo A. Neukomm, Tadd\"aus Strunden, Dan Forster, Morgane Delattre, Shawn Teh, Cl\'ement Rols, John Federice, Hayden Leatherwood, M. Lavelle Barnes, Maarten R. Dobbelaere, Peter Wipf, Jon T. Njardarson, Jieping Zhu, Philippe Schwaller
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