首页|Findings from University of Michigan Has Provided New Data on Machine Learning (Atomistic Simulations and Machine Learning of Solute Grain Boundary Segregation In Mg Alloys At Finite Temperatures)
Findings from University of Michigan Has Provided New Data on Machine Learning (Atomistic Simulations and Machine Learning of Solute Grain Boundary Segregation In Mg Alloys At Finite Temperatures)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on Machine Learning. According to news originatingfrom Ann Arbor, Michigan, by NewsRx correspondents, research stated, “Understanding solute segregationthermodynamics is the first step in investigating grain boundary (GB) properties, such as strong yttrium(Y) effects on grain growth and texture evolution in micro-scale polycrystalline magnesium (Mg) alloys.To estimate the average GB segregation behavior in low-solute-concentration Mg alloys (e.g., 2 at.% Y), astate-of-the-art spectral approach is applied based on a per-site segregation energy spectrum for Y soluteatoms at zero K obtained from molecular statistics (MS) simulations of-104 GB sites in Mg symmetric tiltGBs (STGBs).”
Ann ArborMichiganUnited StatesNorth and Central AmericaAlloysCyborgsEmerging TechnologiesMachine LearningUniversity of Michigan
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