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 ...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.展开更多
Free-standing and fexible air electrodes with long-lasting bifunctional activities for both the oxygen reduction reaction(ORR)and the oxygen evolution reaction(OER)are crucial to the development of wearable Zn-air rec...Free-standing and fexible air electrodes with long-lasting bifunctional activities for both the oxygen reduction reaction(ORR)and the oxygen evolution reaction(OER)are crucial to the development of wearable Zn-air rechargeable batteries.In this work,we synthesize a fexible air electrode consisting of 3D nanoporous N-doped graphene with trimodal shells and Ni particles through repeated chemical vapor deposition(CVD)and acidic etching processes.Our results indicate that such trimodal graphene morphology significantly enhances the active N-dopant sites and graphene-coated Ni surface,which consequentially boosts both the ORR and OER activities,as well as catalytic durability.First-principles density functional theory(DFT)calculations reveal the synergetic effects between the Ni and the N-doped graphene;namely,the Ni nanoparticles boost the bifunctional activities of the coated N-doped graphene,and in turn the graphene-covering layers enhance the stability of Ni.Thanks to the better protection from the triple graphene shells,our trimodal N-doped graphene/Ni-based Zn-air battery can be stably discharged/recharged beyond 2500 h with low overpotentials.It is reasonable to expect that such freestanding trimodal graphene/Ni would be promising in many fexible energy conversion/storage devices.展开更多
The pursuit of designing superconductors with high Tc has been a long-standing endeavor.However,the widespread incorporation of doping in high Tc superconductors significantly impacts electronic structure,intricately ...The pursuit of designing superconductors with high Tc has been a long-standing endeavor.However,the widespread incorporation of doping in high Tc superconductors significantly impacts electronic structure,intricately influencing Tc.The complex interplay between the structural composition and material performance presents a formidable challenge in superconductor design.Based on a novel generative model,diffusion model,and doping adap-tive representation:three-channel matrix,we have designed a high Tc super-conductors inverse design model called Supercon-Diffusion.It has achieved remarkable success in accurately generating chemical formulas for doped high Tc superconductors.Supercon-Diffusion is capable of generating superconduc-tors that exhibit high Tc and excels at identifying the optimal doping ratios that yield the peak Tc.The doping effectiveness(55%)and electrical neutrality(55%)of the generated doped superconductors exceed those of traditional GAN models by more than tenfold.Density of state calculations on the structures further confirm the validity of the generated superconductors.Additionally,we have proposed 200 potential high Tc superconductors that have not been documented yet.This groundbreaking contribution effectively reduces the search space for high Tc superconductors.Moreover,it successfully establishes a bridge between the interrelated aspects of composition,structure,and prop-erty in superconductors,providing a novel solution for designing other doped materials.展开更多
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 meaningf...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.展开更多
基金financial supports from the Fund of Science and Technology on Reactor Fuel and Materials Laboratory(JCKYS2019201074)the Affiliated Hospital of Putian University,the Shenzhen Fundamental Research Program(JCYJ20220531095404009)+1 种基金the Shenzhen Knowledge Innovation Plan-Fundamental Research(Discipline Distribution)(JCYJ20180507184623297)the Major Science and Technology Infrastructure Project of Material Genome Big-science Facilities Platform supported by Municipal Development and Reform Commission of Shenzhen。
文摘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.
基金financially supported by the Development and Reform Commission of Shenzhen Municipalitythe National Natural Science Foundation of China(Grant Nos.51702031,51871077)+1 种基金the Shenzhen Fundamental Research Program(Grant Nos.JCYJ20180306171644942,JCYJ20180507184623297,KQJSCX20180328165656256)the Innovation Project from Harbin Institute of Technology。
文摘Free-standing and fexible air electrodes with long-lasting bifunctional activities for both the oxygen reduction reaction(ORR)and the oxygen evolution reaction(OER)are crucial to the development of wearable Zn-air rechargeable batteries.In this work,we synthesize a fexible air electrode consisting of 3D nanoporous N-doped graphene with trimodal shells and Ni particles through repeated chemical vapor deposition(CVD)and acidic etching processes.Our results indicate that such trimodal graphene morphology significantly enhances the active N-dopant sites and graphene-coated Ni surface,which consequentially boosts both the ORR and OER activities,as well as catalytic durability.First-principles density functional theory(DFT)calculations reveal the synergetic effects between the Ni and the N-doped graphene;namely,the Ni nanoparticles boost the bifunctional activities of the coated N-doped graphene,and in turn the graphene-covering layers enhance the stability of Ni.Thanks to the better protection from the triple graphene shells,our trimodal N-doped graphene/Ni-based Zn-air battery can be stably discharged/recharged beyond 2500 h with low overpotentials.It is reasonable to expect that such freestanding trimodal graphene/Ni would be promising in many fexible energy conversion/storage devices.
基金support from the fund of Science and Technology on Reactor Fuel and Materials Laboratory(JCKYS2019201074)the Guangdong Basic and Applied Basic Research Foundation(2022A1515110676)+4 种基金the Shenzhen Science and Technology Program(JCYJ20220531095404009,RCBS20221008093057027,JCYJ20230807094313028,JCYJ20230807094318038)the Natural Science Foundation of China(Grant No.62102118)the Project of Educational Commission of Guangdong Province of China(Grant No.2021KQNCX274)the Shenzhen Colleges and Universities Stable Support Program(Grant No.GXWD20220811170504001)the Project Supported by Sunrise(Xiamen)Photovoltaic Industry Co.,Ltd.(Development of Artificial Intelligence Technology for Perovskite Photovoltaic Materials,No.HX20230176).
文摘The pursuit of designing superconductors with high Tc has been a long-standing endeavor.However,the widespread incorporation of doping in high Tc superconductors significantly impacts electronic structure,intricately influencing Tc.The complex interplay between the structural composition and material performance presents a formidable challenge in superconductor design.Based on a novel generative model,diffusion model,and doping adap-tive representation:three-channel matrix,we have designed a high Tc super-conductors inverse design model called Supercon-Diffusion.It has achieved remarkable success in accurately generating chemical formulas for doped high Tc superconductors.Supercon-Diffusion is capable of generating superconduc-tors that exhibit high Tc and excels at identifying the optimal doping ratios that yield the peak Tc.The doping effectiveness(55%)and electrical neutrality(55%)of the generated doped superconductors exceed those of traditional GAN models by more than tenfold.Density of state calculations on the structures further confirm the validity of the generated superconductors.Additionally,we have proposed 200 potential high Tc superconductors that have not been documented yet.This groundbreaking contribution effectively reduces the search space for high Tc superconductors.Moreover,it successfully establishes a bridge between the interrelated aspects of composition,structure,and prop-erty in superconductors,providing a novel solution for designing other doped materials.
基金supported by Guangdong Basic and Applied Basic Research Foundation(2022A1515110676 and2024A1515011845)Shenzhen Science and Technology Program(JCYJ20220531095404009,RCBS20221008093057027,and JCYJ20230807094313028)the Project Supported by Sunrise(Xiamen)Photovoltaic Industry Co.,Ltd.(Development of Artificial Intelligence Technology for Perovskite Photovoltaic Materials,HX20230176)。
文摘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.
