摘要
机器人与机器学习每日新闻的一位新闻记者兼新闻编辑-机器学习的最新研究结果已经发表。根据《中国人民日报武汉消息》，NewsRx记者的研究表明：“声子特性之一，声子截止频率，属于材料中最强键的振动频率，它直接影响介质击穿强度。利用光梯度增强机(LightGBM)方法，仅利用15个与材料结构和元素信息有关的特征，就实现了声子截止频率的精确预测。本研究的资金来源于国家重点研究开发项目。我们的新闻记者从武汉理工大学的研究中获得了一句话：“LightGBM模型的性能为0.973的2，RMS E为2.214，MAE为1.289，通过SHapley相加解释(SHAP)，分析表明声子截止频率与组分中元素间最小原子序数之间存在密切的相关性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news originating from Wuhan, People’s Repu blic of China, by NewsRx correspondents, research stated, “One of the phonon pro perties, the phonon cutoff frequency, pertains to the vibration frequency of the strongest bond in a material, and it has a direct impact on the dielectric brea kdown strength. In this study, the accurate prediction of the phonon cutoff freq uency was achieved using the Light Gradient Boosting Machine (LightGBM) methodol ogy, utilizing only 15 features related to the structural and elemental informat ion of materials.” Financial support for this research came from National Key Research and Devel- o pment Program of China. Our news journalists obtained a quote from the research from the Wuhan Universit y of Technology, “The performance of the LightGBM model yielded R2 of 0.973, RMS E of 2.214, and MAE of 1.289, surpassing other models by a significant margin. F eature analysis revealed a close correlation between the phonon cutoff frequency and the minimum of atomic number among the elements in the composition through SHapley Additive exPlanations (SHAP).”
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