The Artificial Intelligence-Driven Intelligent Laboratory for Organic Chemistry Synthesis - Tan Li (Applied Sciences, 2025)
The deep integration and application of artificial intelligence to organic chemistry are propelling the development of organic chemistry synthesis laboratories toward an intelligent automated laboratory model characterized by “hardware + software + AI”. This paper systematically explores the overall framework of AI-driven intelligent laboratories for organic chemistry synthesis, achieving automation and flexibility through standardized experimental integration workstations and intelligent scheduling and collaborative management of experimental resources. By leveraging multimodal databases, the integration of large models, machine learning, and other AI technologies enables AI-driven closed-loop intelligent chemical experiments, including product prediction, molecular retrosynthetic planning, and synthesis reaction optimization. The paper proposes a cloud-based shared operational model for chemical laboratories, aiming to achieve socialized sharing and intelligent matching of experimental resources, thereby facilitating the accumulation and sharing of chemical experimental data to promote the intelligent development of organic chemistry synthesis experiments. Practical cases of building intelligent chemical laboratories are shared, providing paths for technology implementation in constructing the next generation of automated and intelligent chemical laboratories.