氢化物超导体临界转变温度的机器学习模型
Machine Learning Model for Predicting the Critical Transition Temperature of Hydride Superconductors
氢化物超导体临界转变温度的机器学习模型 |
| 赵晋彬, 王建韬, 何东昌, 李俊林, 孙岩, 陈星秋, 刘培涛 |
|
Machine Learning Model for Predicting the Critical Transition Temperature of Hydride Superconductors |
| ZHAO Jinbin, WANG Jiantao, HE Dongchang, LI Junlin, SUN Yan, CHEN Xing-Qiu, LIU Peitao |
| 图4 Tc与组成元素的价电子数的标准差、平均共价半径、Mendeleev数范围，及Fermi能级处H的电子态密度占比4个特征的相关性 |
| Fig.4 Correlation between Tc and four features of Avg_dev(NValence) (a), Mean(CovalentRadius) (b), Range(Mendeleev Number) (c), and HDOS Fraction (d) |
|
|