This two-part review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this first part, we describe a classification for discoveries of physical matter (molecules, materials, devices), processes, and models and how they are unified as search problems. We then introduce a set of questions and considerations relevant to assessing the extent of autonomy. Finally, we describe many case studies of discoveries accelerated by or resulting from computer assistance and automation from the domains of synthetic chemistry, drug discovery, inorganic chemistry, and materials science. These illustrate how rapid advancements in hardware automation and machine learning continue to transform the nature of experimentation and modelling.
Part two reflects on these case studies and identifies a set of open challenges for the field.
BayesEvolve: Explicit Belief States for Autonomous Scientific Discovery
Pfizer（辉瑞公司）；Independent Researcher（独立研究员）；University of California San Diego（加州大学圣地亚哥分校）；Institute of Humanities and Social Science Data, Fudan University（复旦大学人文社会科学数据研究院）
Autonomous discovery of traffic laws with AI traffic scientists
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；China Telecom Research Institute（中国电信研究院）；Macau Institute of Systems Engineering, Macau University of Science and Technology（澳门科技大学澳门系统工程研究所）；State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所复杂系统管理与控制国家重点实验室）
Agentic AI Scientists Are Not Built For Autonomous Scientific Discovery
Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi（印度理工学院德里人工智能学院）；Department of Computer Science and Engineering, Indian Institute of Technology Delhi（印度理工学院德里计算机科学与工程系）；Department of Civil and Environmental Engineering, Indian Institute of Technology Delhi（印度理工学院德里土木与环境工程系）；Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena（耶拿弗里德里希·席勒大学有机与大分子化学实验室）；Center for Energy and Environmental Chemistry Jena, Friedrich Schiller University Jena（耶拿弗里德里希·席勒大学能源与环境化学中心）；Helmholtz Institute for Polymers in Energy Applications Jena (HIPOLE Jena)（耶拿海德堡聚合物能源应用研究所（HIPOLE耶拿））
End-to-end autonomous scientific discovery on a real optical platform
State Key Laboratory of Extreme Photonics and Instrumentation, College of Information Science and Electronic Engineering, ZJU-Hangzhou Global Scientific and Technological Innovation Center, The Electromagnetics Academy at Zhejiang University, Zhejiang University（极端光子学与仪器国家重点实验室，信息科学与电子工程学院，浙大杭州国际科学技术创新中心，浙江大学电磁学学院，浙江大学）
Socratic agents for autonomous scientific discovery in high-dimensional physical systems
Hangzhou Institute for Advanced Study（杭州高等研究 institute）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Institute of Optics and Fine Mechanics（上海光学精密机械研究所）；Department of Thoracic Surgery（胸外科部门）；Shanghai Pulmonary Hospital（上海 pulmonary 医院）；School of Medicine（医学院）；Tongji University（同济大学）
Evaluating Retrieval-Augmented Generation Agents for Autonomous Scientific Discovery in Astrophysics
Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom（英国剑桥大学天文研究所）；Kavli Institute for Cosmology, University of Cambridge, Cambridge, United Kingdom（英国剑桥大学卡弗里宇宙研究所）；Department of Physics, University of Cambridge, Cambridge, United Kingdom（英国剑桥大学物理系）；Center for Computational Astrophysics, Flatiron Institute, New York, NY, USA（Flatiron研究所计算天体物理学中心）；Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA（普林斯顿大学天体物理学系）
Adaptive AI decision interface for autonomous electronic material discovery
Pritzker School of Molecular Engineering, The University of Chicago（芝加哥大学普利兹克分子工程学院）；Nanoscience and Technology Division, Argonne National Laboratory（阿贡国家实验室纳米科学与技术部）；Advanced Light Source, Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室先进光源）；Department of Physics, University of Central Florida（中佛罗里达大学物理系）
This two-part review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this second part, we reflect on a selection of exemplary studies. It is increasingly important to articulate what the role of automation and computation has been in the scientific process and how that has or has not accelerated discovery. One can argue that even the best automated systems have yet to ``discover'' despite being incredibly useful as laboratory assistants. We must carefully consider how they have been and can be applied to future problems of chemical discove…
Bio AI Agent: A Multi-Agent Artificial Intelligence System for Autonomous CAR-T Cell Therapy Development with Integrated Target Discovery, Toxicity Prediction, and Rational Molecular Design
Bio LIMS INC（Bio LIMS公司）
针对CAR-T疗法开发周期长、临床损耗率高的问题，提出多智能体系统Bio AI Agent，通过六个专业智能体协作自主完成靶点选择、毒性预测等CAR-T开发全流程，回顾性验证效果显著，可加速免疫疗法转化。
