An artificial intelligence accelerated ab initio molecular dynamics dataset for electrochemical interfaces

An artificial intelligence accelerated ab initio molecular dynamics dataset for electrochemical interfaces

Abstract Understanding atomic-scale structures at electrochemical interfaces is essential for advancing research and applications in electrochemistry. While experiments can provide detailed microscopic insights, their complexity and inefficiency often limit the large-scale generation of data. Comple...

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Main Authors: Yong-Bin Zhuang, Chang Liu, Jia-Xin Zhu, Jin-Yuan Hu, Jia-Bo Le, Jie-Qiong Li, Xiao-Jian Wen, Xue-Ting Fan, Mei Jia, Xiang-Ying Li, Ao Chen, Lang Li, Zhi-Li Lin, Wei-Hong Xu, Jun Cheng
Format: Article
Language:English
Published: Nature Portfolio 2025-06-01
Series:Scientific Data
Online Access:https://doi.org/10.1038/s41597-025-05338-5
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https://doi.org/10.1038/s41597-025-05338-5

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