首页|Shenzhen University Reports Findings in Machine Learning (Predictingreaction ki netics of reactive bromine species with organic compounds by machine learning: F eature combination and knowledge transfer with reactive chlorine species)
Shenzhen University Reports Findings in Machine Learning (Predictingreaction ki netics of reactive bromine species with organic compounds by machine learning: F eature combination and knowledge transfer with reactive chlorine species)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Machine Learning is th e subject of a report. According to newsreporting originating from Shenzhen, Pe ople’s Republic of China, by NewsRx correspondents, researchstated, “Reactive b romine species (RBS) such as bromine atom (Br) and dibromine radical (Br) are important oxidative species accounting for the transformation of organic compounds in bromide-containingwater. This study developed quantitative structure-activi ty relationship (QSAR) models to predict secondorder rate constants (k) of RBS by machine learning (ML) and conducted knowledge transfer between RBSand reacti ve chlorine species (RCS, e.g., Cl and Cl) to improve model performance.”
ShenzhenPeople’s Republic of ChinaAs iaBromineChlorineCyborgsEmerging TechnologiesHalogensMachine Learnin g
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