首页|Researchers from Peking University Report Findings in Machine Learning (Investig ating Interfacial Segregation of 52 /al In Al-cu Alloys: a Comprehensive Study U sing Density Functional Theory and Machine Learning)
Researchers from Peking University Report Findings in Machine Learning (Investig ating Interfacial Segregation of 52 /al In Al-cu Alloys: a Comprehensive Study U sing Density Functional Theory and Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting out of Beijing, People’s Re public of China, by NewsRx editors, research stated, “Solute segregation at the interface between the aluminum (Al) matrix and the 52 ( Al 2 Cu ) phase decrease s the interfacial energy, impedes the coarsening of precipitates, and enhances t he thermal stability of such precipitates. In this study, we employ density func tional theory to systematically calculate solute segregation energies of 42 solu te elements at the coherent and semi-coherent interfaces between the two phases, as well as mixing energies of these elements within the Al and Cu sublattices o f the 52 phase.”
BeijingPeople’s Republic of ChinaAsiaAlloysCyborgsEmerging TechnologiesMachine LearningPeking University
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