首页|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)
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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.”
ChongqingPeople’s Republic of ChinaA siaAluminumCyborgsEmerging TechnologiesLight MetalsMachine LearningC hongqing University