首页|Chinese Academy of Sciences Researchers Update Knowledge of Machine Learning (Ma chine-learning-driven simulations on microstructure, thermodynamic properties, a nd transport properties of LiCl-KCl-LiF molten salt)
Chinese Academy of Sciences Researchers Update Knowledge of Machine Learning (Ma chine-learning-driven simulations on microstructure, thermodynamic properties, a nd transport properties of LiCl-KCl-LiF molten salt)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news originating from Ningbo, Peo ple's Republic of China, by NewsRx editors, the research stated, "The thermodyna mic and transport properties of high-temperature chloride molten salt systems ar e of great significance for spent fuel reprocessing in the field of nuclear ener gy engineering." Financial supporters for this research include National Natural Science Foundati on of China. Our news editors obtained a quote from the research from Chinese Academy of Scie nces: "Here, by using machine learning based deep potential (DP) method, we trai n a high-precision force field model for the LiCl-KCl-LiF system. During force f ield training, adding new dataset through multiple iterations improves the accur acy of the force field model and its applicability to more configurations. The c omparison of density functional theory (DFT) and DP results for the test dataset indicates that our trained DP model has the same accuracy as DFT. Then, we comp rehensively investigate the local structure, thermophysical properties, and tran sport properties of the LiCl-KCl and LiCl-KCl-LiF molten salt systems using the trained DP model. The effects of temperature and LiF concentration on the above properties are analyzed."
Chinese Academy of SciencesNingboPeo ple's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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