Findings from Chongqing University Update Knowledge of Machine Learning (Predict ing the Grain Boundary Segregation Energy of Solute Atoms In Aluminum By First-p rinciples Calculation and Machine Learning)
Findings from Chongqing University Update Knowledge of Machine Learning (Predict ing the Grain Boundary Segregation Energy of Solute Atoms In Aluminum By First-p rinciples Calculation and Machine Learning)
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Researchers detail new data in Machine Learning. According to news reporting originatingfrom Chongqing, People’s Repu blic of China, by NewsRx correspondents, research stated, “Grainboundary (GB) s egregation energy is an important factor affecting the segregation behavior of s oluteatoms and the mechanical properties of alloys. In this study, first-princi ples calculation combined withmachine learning (ML) algorithms were used to cal culate and predict the GB segregation energies of soluteatoms in Al alloys.”
Key words
Chongqing/People’s Republic of China/A sia/Aluminum/Cyborgs/Emerging Technologies/Light Metals/Machine Learning/C hongqing University
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