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2 单次训练下临界温度(Tc)的模型测试误差与特征数量的关系，及10次独立训练下的平均模型测试集误差与训练集占比的关系

Fig.2 Model test errors as a function of number of features for one single training (a) and averaged model test errors of ten independent trainings as a function of training set ratio (b) (The shadow-filled areas show the standard deviation. MAE—mean absolute error, RMSE—root mean square error)