Deep Generative Model for Inverse Design of High-Temperature Superconductor Compositions with Predicted <i>T</i><sub>c</sub>

Deep Generative Model for Inverse Design of High-Temperature Superconductor Compositions with Predicted T_c > 77 K

Author:

Zhong Chengquan¹²^ORCID,Zhang Jingzi¹²^ORCID,Lu Xiaoting¹²,Zhang Ke¹²,Liu Jiakai¹²^ORCID,Hu Kailong¹³²^ORCID,Chen Junjie⁴,Lin Xi¹³²^ORCID

Affiliation:

1. School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China

2. Blockchain Development and Research Institute, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China

3. State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China

4. Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China

Funder

Basic and Applied Basic Research Foundation of Guangdong Province

Science, Technology and Innovation Commission of Shenzhen Municipality

Department of Education of Guangdong Province

National Natural Science Foundation of China

Shenzhen Colleges and Universities Stable Support Program

Science and Technology on Reactor Fuel and Materials Laboratory

Publisher

American Chemical Society (ACS)

Subject

General Materials Science

Link

Reference47 articles.

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