首页|Studies from Carnegie Mellon University Further Understanding of Machine Learnin g (Surface Segregation Studies In Ternary Noble Metal Alloys: Comparing Dft and Machine Learning With Experimental Data)
Studies from Carnegie Mellon University Further Understanding of Machine Learnin g (Surface Segregation Studies In Ternary Noble Metal Alloys: Comparing Dft and Machine Learning With Experimental Data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting from Pittsburgh, Pennsylvania, by N ewsRx journalists, research stated, "Surface segregation, whereby the surface co mposition of an alloy differs systematically from the bulk, has historically bee n hard to study, because it requires experimental and modeling methods that span alloy composition space. In this work, we study surface segregation in catalyti cally relevant noble and platinum-group metal alloys with a focus on three terna ry systems: AgAuCu, AuCuPd, and CuPdPt." Financial supporters for this research include National Energy Research Scientif ic Computing Center (NERSC), United States Department of Energy (DOE), NSF DMREF Award.
PittsburghPennsylvaniaUnited StatesNorth and Central AmericaAlloysCyborgsEmerging TechnologiesMachine Lear ningCarnegie Mellon University
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