首页|Reports Summarize Machine Learning Findings from Universityof Jinan (Transfer L earning-assisted Computational Screening ofMetal-organic Frameworks and Covalen t-organic Frameworks for the Separation of Xe/kr Noble Gas)
Reports Summarize Machine Learning Findings from Universityof Jinan (Transfer L earning-assisted Computational Screening ofMetal-organic Frameworks and Covalen t-organic Frameworks for the Separation of Xe/kr Noble Gas)
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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 reporting out of Zhuhai, People ’s Republic of China, by NewsRx editors, research stated, “Theadsorption separa tion of xenon/krypton (Xe/Kr) by porous materials has gained significant attenti onin industrial development. Metal-organic frameworks (MOFs) and covalent-organ ic frameworks (COFs)featuring permanent porosity, ultra-high surface area, and tunable pore size enable efficient separation of Xe/Kr, which promising structur es can be identified by high-throughput computational screening andmachine lear ning.”
ZhuhaiPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningUniversity of Jinan
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