摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。据新闻报道研究称，NewsRx记者源于中华人民共和国长沙的报道，“(HECCs)高熵硬质合金陶瓷的广阔组成空间使传统的实验变得更加直观”试验误差和计算模拟不足以快速跟踪SAL和筛选。在这项工作中，利用可检索的低成本物理力学参数作为特征输入，我们已经成功地建立了一个优秀的预测机器模型（精度=0.938，召回率=0.994，F1得分=0.953）HECCs与目前报道的基于密度泛函的优秀模型相当理论数据，利用机器学习技术"。
Abstract
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.”
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