基于大语言模型的下一代AI实验室研究进展OA

The rapid development of artificial intelligence(AI)and robotics has driven an increase in laboratory automa-tion,transforming traditional manual experiments into machine-driven processes and enhancing experimental efficiency and reproducibility.Machine learning algorithms have facilitated the automation of synthesis,testing,and catalysis in laboratories,evolving towards a comprehensive workflow that encompasses synthesis,characterization,and performance testing.Large language model(LLM),with their advantages in precise information filtering,long sequence modeling,and the ability to parse complex semantic relationships,has further improved the intelligence and automation of laboratories.LLM integrates knowledge from multiple fields for experimental design and converts procedures into executable code.The robot then executes operations according to instructions,achieving functional synergy such as theoretical calcula-tions,algorithm design,and automated experiments,leading a new paradigm in scientific research.Consequently,these intelligent systems that autonomously conduct experimental design and scientific research are referred to as"next-generation AI laboratory".This paper elaborates on the principles and applications of LLM,summarizes the development process of AI-enabled laboratories,emphasizes the pattern and outcomes of the deep integration of LLM with AI laboratories,and finally offers a perspective on the opportunities and challenges ahead.