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
(a) Li2MgH16 (250 GPa) (b) CaHfH12 (190 GPa) (c) CaHfH18 (300 GPa)
(d) CaZrH12 (300 GPa) (e) MgH12 (500 GPa) (f) YH10 (250 GPa)
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 |
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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5 数据集中6个Tc最高的氢化物超导材料的晶体结构和电子态密度 |
| Fig.5 Crystal structures and electronic densities of states for the six hydride superconductors with the highest Tc in the dataset (a) Li2MgH16 (250 GPa) (b) CaHfH12 (190 GPa) (c) CaHfH18 (300 GPa) (d) CaZrH12 (300 GPa) (e) MgH12 (500 GPa) (f) YH10 (250 GPa) |
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