Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Jin-Da Luo"'
Publikováno v:
Artificial Intelligence Chemistry, Vol 2, Iss 1, Pp 100051- (2024)
Solid-state electrolytes are key ingredients in next-generation devices for energy storage and release. Machine learning molecular dynamics (MLMD) has shown great promise in studying the diffusivity of mobile ions in solid-state electrolytes, with mu
Externí odkaz:
https://doaj.org/article/b41780c92aa7462db60552ca35bb070e
Autor:
Jun-Nan Yang, Zhen-Yu Ma, Jin-Da Luo, Jing-Jing Wang, Chunyin Ye, Yujie Zhou, Yi-Chen Yin, Xue-Chen Ru, Tian Chen, Lian-Yue Li, Li-Zhe Feng, Kuang-Hui Song, Jing Ge, Qun Zhang, Hong-Bin Yao
Publikováno v:
Nano Letters. 23:3385-3393
Autor:
Yi-Chen Yin, Jing-Tian Yang, Jin-Da Luo, Gong-Xun Lu, Zhongyuan Huang, Jian-Ping Wang, Pai Li, Feng Li, Ye-Chao Wu, Te Tian, Yu-Feng Meng, Hong-Sheng Mo, Yong-Hui Song, Jun-Nan Yang, Li-Zhe Feng, Tao Ma, Wen Wen, Ke Gong, Lin-Jun Wang, Huan-Xin Ju, Yinguo Xiao, Zhenyu Li, Xinyong Tao, Hong-Bin Yao
Publikováno v:
Nature. 616:77-83
Autor:
Hongsheng Mo, Yi-Chen Yin, Jin-Da Luo, Jing-Tian Yang, Feng Li, Dong-Mei Huang, Hongjun Zhang, Bangjiao Ye, Te Tian, Hong-Bin Yao
Publikováno v:
ACS Applied Materials & Interfaces. 14:17479-17485
Exploring new solid electrolytes (SEs) for lithium-ion conduction is significant for the development of rechargeable all-solid-state lithium batteries. Here, a lead-free organic-inorganic halide perovskite, MASr
Autor:
Feng Li, Jin-Da Luo, Le Tang, Xiaobin Cheng, Guozhen Zhang, Yi-Chen Yin, Te Tian, Hongsheng Mo, Jing-Tian Yang, Hong-Bin Yao
Publikováno v:
ACS Applied Energy Materials. 5:4926-4933
Autor:
Feng Li, Xiaobin Cheng, Lei-Lei Lu, Yi-Chen Yin, Jin-Da Luo, Gongxun Lu, Yu-Feng Meng, Hongsheng Mo, Te Tian, Jing-Tian Yang, Wen Wen, Zhi-Pan Liu, Guozhen Zhang, Cheng Shang, Hong-Bin Yao
Publikováno v:
Nano letters. 22(6)
Solid electrolytes (SEs) with superionic conductivity and interfacial stability are highly desirable for stable all-solid-state Li-metal batteries (ASSLMBs). Here, we employ neural network potential to simulate materials composed of Li, Zr/Hf, and Cl