首页|Study Results from National University of Defense Technology Provide New Insight s into Machine Learning (Accelerated Discovery and Formation Mechanism of High-e ntropy Carbide Ceramics Using Machine Learning Based On Low-cost Descriptors)
Study Results from National University of Defense Technology Provide New Insight s into Machine Learning (Accelerated Discovery and Formation Mechanism of High-e ntropy Carbide Ceramics Using Machine Learning Based On Low-cost Descriptors)
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 DailyNews Daily News – New research on Machine Learning is th e subject of a report. According to newsreporting originating in Changsha, Peop le’s Republic of China, by NewsRx journalists, research stated,“The vast compos itional space of High-entropy carbide ceramics (HECCs) renders traditional exper imentaltrialand-error and computational simulations inadequate for rapid traver sal and screening. In this work,leveraging retrievable and low-cost physicochem ical parameters as feature inputs, we have successfullyestablished an excellent predictive machine model (precision = 0.938, recall = 0.994, F1 score = 0.953) forHECCs, which is comparable to excellent model currently reported that was tr ained on density functionaltheory data, utilizing machine learning techniques.”
ChangshaPeople’s Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningNational University of Def ense Technology
NETL
NSTL
