Crystal structure graph neural networks for high-performance superconducting critical temperature prediction

晶体结构图神经网络用于高性能超导临界转变温度的预测

The utilization of machine learning methods to predict the superconducting critical temperature(T_(c))traditionally necessitates manually constructing elemental features,which challenges both the provision of meaningful chemical insights and the accuracy of predictions.In this work,we introduced crystal structure graph neural networks to extract structure-based features for T_(c)prediction.Our results indicated that these structure-based models outperformed all previously reported models,achieving an impressive coefficient of determination(R^(2))of 0.962 and a root mean square error(RMSE)of 6.192 K.From the existing Inorganic Crystal Structure Database(ICSD),our model successfully identified 76 potential high-temperature superconducting compounds with T_(c)≥77 K.Among these,Tl_(5)Ba_(6)Ca_(6)Cu_(9)O_(29)and TlYBa_(2)Cu_(2)O_(7)exhibit remarkably high T_(c)values of 108.4 and 101.8 K,respectively.This work provides a perspective on the structure-property relationship for reliable T_(c)prediction.

机器学习预测超导材料的临界转变温度(T_(c))一般需要通过超导材料的化学式来构建元素特征,而缺乏对晶体结构的充分考虑,限制了机器学习模型对超导材料结构-性能关系的探究.本工作建立了超导材料晶体结构数据集,通过图神经网络提取晶体结构特征构建机器学习模型进行T_(c)预测.结果表明使用晶体结构特征模型的T_(c)预测决定系数(R^(2))达0.962,均方根误差仅为6.192 K,预测效果优于使用元素特征模型.模型从无机晶体结构数据库中挖掘出76种T_(c)≥77 K的潜在高温超导材料,其中Tl_(5)Ba_(6)Ca_(6)Cu_(9)O_(29)和TlYBa_(2)Cu_(2)O_(7)表现出较高的T_(c)值,分别为108.4和101.8 K.本工作从结构-性能角度为T_(c)预测提供了新思路.