An integrated machine learning model for accurate and robust prediction of superconducting critical temperature

Discovering new superconductors via traditional trial-and-error experimental approaches is apparently a time-consuming process,and the correlations between the critical temperature(Tc) and material features are still obscure.The rise of machine learning(ML) technology provides new opportunities to speed up inefficient exploration processes,and could potentially uncover new hints on the unclear correlations.In this work,we utilize open-source materials data,ML models,and data mining methods to explore the correlation between the chemical features and Tcvalues of superconducting materials.To further improve the prediction accuracy,a new model is created by integrating three basic algorithms,showing an enhanced accuracy with the coefficient of determination(R2) score of 95.9 % and root mean square error(RMSE) of 6.3 K.The average marginal contributions of material features towards Tcvalues are estimated to determine the importance of various features during prediction processes.The results suggest that the range thermal conductivity plays a critical role in Tcprediction among all element features.Furthermore,the integrated ML model is utilized to screen out potential twenty superconducting materials with Tcvalues beyond 50.0 K.This study provides insights towards Tcprediction to accelerate the exploration of potential high-Tcsuperconductors.

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

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.

Strong-coupling superconductivity with Tc above 70 K in Be-decorated monolayer T-graphene

Using first-principles calculations,we predict a new type of two-dimensional(2D)beryllium(Be)-decorated T-graphene named BeC_(2),where Be atoms are inserted into C–C bonds linking the carbon tetrarings of T-graphene.The band structure shows that BeC_(2)is metallic,thus,the possible phonon-mediated superconductivity is explored based on the Eliashberg equation.The calculated electron-phonon coupling(EPC)constantλis up to 4.07,and the corresponding superconducting critical temperature(Tc)is 72.1 K,approaching the liquid nitrogen temperature.The reason for the high Tc is the strong EPC.And it is proved to be an anisotropic single-gap superconductor by analyzing the superconducting gap?kof BeC_(2).The electronic susceptibility calculation shows strong nesting effect in BeC_(2).Since rare 2D superconductors show such a strong EPC constantλwhich originates from the coupling between electrons in C-pzorbital and in-plane vibrations of Be and C atoms,the predicted BeC_(2)provides a new platform for investigating strong EPC 2D superconductor.