Deep Learning Enhanced in Situ Atomic Imaging of Ion Migration at Crystalline–Amorphous Interfaces

Improving the performance of energy storage, neuromorphic computing, and more applications requires an in-depth understanding of ion transport at interfaces, which are often hindered by facile atomic reconfiguration at working conditions and limited characterization capability. Here, we construct an...

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Bibliographic Details
Main Author:	Weikang Dong (10776392) (author)
Other Authors:	Yi-Chi Wang (4966093) (author), Chen Yang (207381) (author), Chunhao Sun (12901189) (author), Hesong Li (20107022) (author), Ze Hua (17513712) (author), Ziqi Wu (496313) (author), Xiaoxue Chang (20107025) (author), Lixia Bao (10058355) (author), Shuangquan Qu (9603683) (author), Xintao Zuo (3556469) (author), Wen Yang (235745) (author), Jing Lu (120666) (author), Ying Fu (173963) (author), Jiafang Li (3957740) (author), Lixin Dong (1486483) (author), Ruiwen Shao (5704031) (author)
Published:	2024
Subjects:	Biophysics Biochemistry Medicine Cell Biology Chemical Sciences not elsewhere classified unique methodology paves unexpected chirality changes novel metastable state limited characterization capability based image enhancement facile atomic reconfiguration potassium ion insertion situ atomic imaging potassium ion migration 4 </ sub deep learning enhanced ion migration deep learning atomic images situ double ion transport enhanced 3 working conditions traditional methods reaction pathway rational design often hindered neuromorphic computing nanostructured materials microstructure evolution fold compared energy storage depth understanding combined experiments applications requires antimony selenide
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Deep Learning Enhanced in Situ Atomic Imaging of Ion Migration at Crystalline–Amorphous Interfaces

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