首页|Chinese Academy of Sciences Reports Findings in Machine Learning (Prediction of the Energetics of Stable Self-interstitial Atoms At Tungsten Grain Boundaries Vi a Machine Learning)
Chinese Academy of Sciences Reports Findings in Machine Learning (Prediction of the Energetics of Stable Self-interstitial Atoms At Tungsten Grain Boundaries Vi a Machine Learning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are pre sented in a new report. According to news reporting originating from Hefei, Peop le’s Republic of China, by NewsRx correspondents, research stated, “The stable s ites of self-interstitial atoms (SIAs) at grain boundaries (GBs) and correspondi ng energetics are fundamental to explore both the SIA-GB interaction and the eff ects of SIA segregation on the role of GBs in microstructural evolution under ir radiation. In this study, by combing the support vector machine (SVM) model suit able for handling small samples with the differential evolution (DE) algorithm d esigned for global optimization, a machine learning (ML) framework for modeling and predicting the formation energies of stable SIAs (EfSIAmin) at tungsten GBs was established.”
HefeiPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningTransition ElementsTungstenChinese Academy of Sciences
NETL
NSTL
