首页|Findings on Machine Learning Reported by Investigators at Beijing University of Chemical Technology (Combining Machine Learning and Molecular Simulation To Expl ore Mof Materials That Contribute To Cf4/n2 Separation)
Findings on Machine Learning Reported by Investigators at Beijing University of Chemical Technology (Combining Machine Learning and Molecular Simulation To Expl ore Mof Materials That Contribute To Cf4/n2 Separation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting originating from Beijing, People's Republ ic of China, by NewsRx correspondents, research stated, "Highthroughput molecul ar simulations and machine learning algorithms have been widely used to identify promising metal-organic frameworks for gas separation. However, most studies ar e limited to screening highperformance materials from existing databases, which fails to fully utilize the predictive function of machine learning." Financial support for this research came from National Key R & D P rogram of China. Our news editors obtained a quote from the research from the Beijing University of Chemical Technology, "This paper combines genetic algorithms and high-through put screening to deeply mine anion-pillared MOF (APMOF) performance feature rela tionships and predict high-performance materials that are not in the database. C onsidering the actual industrial conditions (CF4/N2 = 10/90), we chose the ratio of CF4:N2 = 1:9 to simulate the adsorption separation of gas mixtures of MOF ma terials at a temperature of 298 K and a pressure of 1 bar. First, the CF4/N2 sep aration properties of MOFs in the APMOF library were obtained based on molecular simulations. Then, the filtered data were coded according to the method of ‘bui lding block classification structural interval categorization'. Then, the machin e learning algorithm was used for model training to obtain a high-precision mode l. Finally, the tangent adaptive genetic algorithm was used to recombine the gen es of the materials, and the new MOF materials were successfully reverse-enginee red. The study found that the pore-limit diameter of APMOFs is most conducive to the separation of CF4/N2 by MOFs when the pore-limit diameter of APMOFs is with in two times the molecular dynamics diameter of CF4. 134 MOF materials were pred icted to have CF4/N2 selectivities exceeding 46.30. The use of organic ligands s uch as 4,4 ‘-bipyridyl or 1,4-bis(4-pyridyl)benzene (bpb) increases the likeliho od of these materials being high-performance for CF4/N2 separation. The combinat ion of computational screening methods and machine learning can expedite the des ign and development of new high-performance MOFs."
BeijingPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningBeijing University of Chemi cal Technology
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