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3 递归筛选出的最重要的4个特征，及所获得的最精确的机器学习模型预测的Tc与真值对比 |
| Fig.3 The most important 4 features by recursive selections (a) and Tc predicted by the most accurate machine learning model obtained vs the ground-truth values (b) ([Avg_dev(NValence)]—standard deviation of the number of valence electrons of constituent element, [Mean(CovalentRadius)]—mean covalent radius of constituent element, [Range(Mendeleev Number)]—range of the Mendeleev number of constituent element, HDOS Fraction—hydrogen fraction of the total density of states at the Fermi energy) |
|
|