首页|New Findings on Machine Learning Described by Investigators at Beijing Universit y of Chemical Technology (Machine Learning Aided Computational Exploration of Me tal-organic Frameworks With Open Cu Sites for the Effective Separation of Hydrog en ...)
New Findings on Machine Learning Described by Investigators at Beijing Universit y of Chemical Technology (Machine Learning Aided Computational Exploration of Me tal-organic Frameworks With Open Cu Sites for the Effective Separation of Hydrog en ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Research findings on Machine Learning are discussed in a new report. According tonews originating from Beijing, Peopl e’s Republic of China, by NewsRx correspondents, research stated,“Efficient sep aration of hydrogen isotopes is of vital importance to develop nuclear energy in dustry,while it remains a significant challenge to separate D2 from H2 due to t heir identical physicochemical properties. As one of the efficient alternatives to conventional techniques, the thermodynamic quantumsieving technology using m etal-organic frameworks (MOFs) featuring open metal sites (OMSs) has showna gre at potential.”
BeijingPeople’s Republic of ChinaAsi aCyborgsElementsEmerging TechnologiesEngineeringGasesHydrogenInorg anic ChemicalsIsotopesMachine LearningBeijing University of Chemical Techn ology
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