首页|Findings from National Institute of Technology Update Knowledge of Machine Learn ing (Temperature-dependent Magnetic Properties of Bcc and Fcc Feni Alloys: a Mac hine Learning Assisted Montecarlo Approach)
Findings from National Institute of Technology Update Knowledge of Machine Learn ing (Temperature-dependent Magnetic Properties of Bcc and Fcc Feni Alloys: a Mac hine Learning Assisted Montecarlo Approach)
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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 Tamil Nadu, India, by NewsRx j ournalists, research stated, “The estimation of exchange integrals in metals and alloys from first-principle calculations yields Curie temperature (TC), which i s not in agreement with the experiments.The microscopic exchange interactions o f the bcc and fcc FeNi alloys are predicted by training large datasets of random magnetization curves simulated using the atomic scale Monte Carlo method with r egression models to accurately reproduce the experimental TC.”
Tamil NaduIndiaAsiaAlloysCyborgsEmerging TechnologiesMachine LearningNational Institute of Technology
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NSTL
