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Advanced science: on demand synthesis -- a scientific experiment platform based on artificial intelligence technology

wallpapers News 2020-12-22

artificial intelligence robot technology are very important to the future manufacturing industry; the main feature of this field is that it can integrate the whole process from virtual modeling design to real-time operation maintenance provide customers with novel perfect products. As the research development stage before the industrial assembly line the laboratory needs more complex variables to optimize the required functional attributes. Usually the research development of material performance optimization needs a lot of labor there are security risks experimental repeatability problems. As early as Da Vinci's time experimental automation programs began to sprout. In modern times automation of chemical laboratory robots can be traced back to the 1980s. At that time robot technology lacked "intelligence" could only hle limited tasks. Nowadays with the promotion of artificial intelligence the effective application of advanced electronic information technology big data algorithms will greatly promote the development of basic research. In recent years robot technology has been widely used in automatic chemical laboratory. Effective use of chemical big data artificial intelligence algorithm to give the robot the ability to independently search reaction path optimize experimental parameters realize independent discovery combined with the understing of scientific theory reduce the method of traversing the parameter space to explore will more improve the work efficiency.

come from Professor Zhu Xi's team of Shenzhen Institute of artificial intelligence robotics have developed a "materials acceleration operation syetem" (materials acceleration operation syetem) which aims at the on-dem synthesis of inorganic nano materials has the function of scientific exploration. Maos has its own unique interface compiler architecture integrating virtual reality cooperative robot reinforcement learning technology. After modeling training in virtual reality Maos can control the experimental hardware to complete the experimental work independently reducing the time labor costs. The team also tested the data communication between laboratory mobile devices in 4G / 5G network. In the framework of reinforcement learning Maos explored an improved quantum dot nucleation theory fed back the optimal strategy which well met the requirements of emission wavelength size distribution of CdSe quantum dots. In addition it can be widely used to synthesize a variety of inorganic nanomaterials (PbS quantum dots perovskite quantum dots Au nanoparticles etc.) based on solution method. The researchers believe that the relationship between the morphology the performance of makdos can be integrated. This work provides a living example for "on dem" material synthesis system which shows how artificial intelligence technology can reshape traditional material science research in the future. This work was published on

of advanced science
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