氢化物超导体临界转变温度的机器学习模型
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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
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ZHAO Jinbin, WANG Jiantao, HE Dongchang, LI Junlin, SUN Yan, CHEN Xing-Qiu, LIU Peitao
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表2 针对数据集中6个Tc最高的氢化物超导材料的模型预测[43,59,60,70,89]
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Table 2 Model predictions for the six hydride superconductors with the highest Tc in the dataset[43,59,60,70,89]
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Material | Pressure GPa | HDOS | Range(Mendeleev Number) | Mean(CovalentRadius) | Avg_dev (NValence) | Tc (RF) K | Tc (Belli)[59] K | Tc (Expt.) K |
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| Li2MgH16 | 250 | 0.53 | 91 | 47.0 | 0.10 | 319.3 | 298.3 | 473.0[43] | | CaHfH12 | 190 | 0.28 | 85 | 51.6 | 2.24 | 324.9 | 198.3 | 363.0[60] | | CaHfH18 | 300 | 0.41 | 85 | 45.5 | 1.62 | 339.0 | 332.0 | 345.0[60] | | CaZrH12 | 300 | 0.31 | 85 | 51.6 | 0.49 | 308.2 | 192.6 | 343.0[60] | | MgH12 | 500 | 0.72 | 24 | 39.5 | 0.14 | 300.5 | 522.4 | 340.0[70] | | YH10 | 250 | 0.41 | 80 | 45.6 | 0.33 | 312.5 | 259.6 | 326.0[89] | | MAE | - | - | - | - | - | 47.6 | 125.3 | - | | RMSE | - | - | - | - | - | 68.4 | 140.3 | - |
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