High-performance diffusion model for inverse design of high Tc superconductors with effective doping and accurate stoichiometry

被引:4
|
作者
Zhong, Chengquan [1 ,2 ]
Zhang, Jingzi [1 ,2 ]
Wang, Yuelin [1 ,2 ]
Long, Yanwu [1 ,2 ]
Zhu, Pengzhou [1 ,2 ]
Liu, Jiakai [1 ,2 ,3 ]
Hu, Kailong [1 ,2 ,4 ]
Chen, Junjie [5 ,6 ]
Lin, Xi [1 ,2 ,4 ,7 ]
机构
[1] Harbin Inst Technol, Sch Mat Sci & Engn, Shenzhen, Guangdong, Peoples R China
[2] Harbin Inst Technol, Blockchain Dev & Res Inst, Shenzhen, Guangdong, Peoples R China
[3] Sunrise Xiamen Photovolta Ind Co Ltd, Xiamen, Fujian, Peoples R China
[4] Harbin Inst Technol, State Key Lab Adv Welding & Joining, Harbin, Heilongjiang, Peoples R China
[5] Harbin Inst Technol, Sch Comp Sci & Technol, Shenzhen, Guangdong, Peoples R China
[6] Harbin Inst Technol, Sch Comp Sci & Technol, Shenzhen 518055, Guangdong, Peoples R China
[7] Harbin Inst ofTechnol, Sch Mat Sci & Engn, Shenzhen 518055, Guangdong, Peoples R China
关键词
diffusion model; generative model; high T-c superconductors; inverse design; machine learning;
D O I
10.1002/inf2.12519
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The pursuit of designing superconductors with high T-c has been a long-standing endeavor. However, the widespread incorporation of doping in high T-c superconductors significantly impacts electronic structure, intricately influencing T-c. 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 adaptive representation: three-channel matrix, we have designed a high T-c superconductors inverse design model called Supercon-Diffusion. It has achieved remarkable success in accurately generating chemical formulas for doped high T-c superconductors. Supercon-Diffusion is capable of generating superconductors that exhibit high T-c and excels at identifying the optimal doping ratios that yield the peak T-c. 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 T-c superconductors that have not been documented yet. This groundbreaking contribution effectively reduces the search space for high T-c superconductors. Moreover, it successfully establishes a bridge between the interrelated aspects of composition, structure, and property in superconductors, providing a novel solution for designing other doped materials. The pursuit of designing superconductors with high Tc has been a long-standing endeavor. Based on a novel generative model, diffusion model, and doping adaptive representation, we have developed a high Tc superconductors inverse design model called Supercon-Diffusion. This model has achieved remarkable success in accurately generating chemical formulas for doped high Tc superconductors. image
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页数:13
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