Robots shuttle back and forth between operating platforms, extend their robotic arms, "hold" test tubes, weigh and sample, and prepare reagents... Entering the Laboratory of Machine Chemists at the University of Science and Technology of China (hereinafter referred to as the University of Science and Technology of China), a new chemical research scene is dazzling. There are no experimental operators wearing white coats here, and the experimental work of bottles, jars, and cans is all completed by a robot. This robot was developed by a team led by Professor Jiang Jun from the Department of Chemical Physics at China University of Science and Technology. It can not only operate experiments based on instructions, but also utilize machine intelligence to search and read literature, draw expert experience from massive research data, propose scientific hypotheses and develop experimental plans based on previous knowledge and data. In addition, the high-quality machine experimental data generated by it can also be integrated with theoretical pre training models to generate intelligent models that integrate theory with reality to find the global optimal solution. Our machine chemists possess a 'super strong chemical brain' that can find optimal solutions from billions of possible combinations, shortening the tasks that scientists cannot complete in their lifetime to just a few weeks, "Jiang Jun told Science and Technology Daily at a recent press conference of the Chinese Academy of Sciences. At present, data-driven artificial intelligence is changing the entire scientific research by making good use of artificial intelligence and big data. Scientists hope to use artificial intelligence and big data technology to liberate their hands, improve the efficiency and accuracy of scientific research, and ultimately lead to more innovative achievements. In the field of chemistry, researchers particularly hope to receive the assistance of big data and artificial intelligence to accelerate material research and development. Speaking of the reasons behind this, Professor Li Zhenyu from China University of Science and Technology told reporters: "In the past 150 years, chemical research has mainly relied on guessing, trying, correcting mistakes, guessing, and trying again... Under this research paradigm, researchers have gradually accumulated materials like stamp collecting, and made discoveries through the process of trial and error. The research efficiency is very low and the cost is high. At the same time, as chemical research objects become increasingly complex and multidimensional, In the face of the huge chemical space, the search for formula and process often stops at local optimization, and cannot carry out global exploration. Therefore, changing the paradigm of chemical research and achieving precision and intelligence in chemical research has always been the dream of chemists. The gradual rise and rapid iterative evolution of big data and artificial intelligence technologies have shown researchers new opportunities to solve these challenges. We hope to take this opportunity to establish a new paradigm for chemical research, and empower artificial intelligence machine chemists with chemical intelligence driven by both precision and intelligence, "said Li Zhenyu. So in 2014, Jiang Jun's team proposed the concept of "machine chemist" and carried out related scientific research work. We have formed an interdisciplinary team covering chemistry, computer science, mathematics, automation, and other fields. After 8 years of research, we have successfully developed a 'full process machine chemist' driven by data intelligence. Jiang Jun introduced that the machine chemist integrates multiple technologies such as mobile robots, chemical workstations, intelligent operating systems, and scientific databases. The lack of high-quality scientific data is a major challenge. Nowadays, machine chemists with a "super strong chemical brain" are demonstrating its value. Zou Gang, a professor at the University of Science and Technology of China who focuses on the research of optical thin film materials, has always wanted to