New Artificial Intelligence Algorithms

According to a report on the website of the National Institute of Standards and Technology on November 24, a multi-institutional team from the National Institute of Standards and Technology, the University of Maryland and the Stanford Linear Accelerator Center (SLAC) of the U.S. Department of Energy has developed a closed-loop material exploration and optimization based on artificial intelligence The system (CAMEO) algorithm aims to use the self-learning characteristics of the algorithm to discover complex new materials with specific properties through fewer experiments, to help scientists minimize the time of trial and error in experiments and improve the efficiency of new material development. The research team connected the X-ray diffraction equipment to a computer equipped with the CAMEO algorithm and imported the existing material database into the algorithm. After many iterations of learning, only a small amount of routine measurement can be used to find The best material for specific properties. Using this method, researchers discovered new nanocomposite phase change memory materials among 177 possible materials. The number of test iterations required was reduced to 1/10 of the original, and the time required was shortened from 90 hours.