首页|Findings from Wuhan University of Technology Reveals New Findings on Machine Lea rning (Machine Learning Assisted Prediction of the Phonon Cutoff Frequency of Ab o3 Perovskite Materials)
Findings from Wuhan University of Technology Reveals New Findings on Machine Lea rning (Machine Learning Assisted Prediction of the Phonon Cutoff Frequency of Ab o3 Perovskite Materials)
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 – 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).”
WuhanPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningWuhan University of Technolog y
NETL
NSTL
