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

Journal: Scientific data
Published Date: Jun 13, 2025

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. Complementing experimental approaches, computational methods, such as ab initio molecular dynamics and machine learning-accelerated molecular dynamics, offer an efficient means of obtaining microscopic information. However, despite these advancements, computational studies of interfaces have typically shared research data in isolation, often through private repositories. This practice has led to fragmented knowledge, reduced data accessibility, and limited opportunities for cross-study comparisons or large-scale meta-analyses. To overcome these challenges, we introduce ElectroFace, an artificial intelligence-accelerated ab initio molecular dynamics dataset for electrochemical interfaces. ElectroFace is designed to compile, visualize, and provide open access to interface data, fostering collaboration and accelerating progress in the field.

Authors

  • Yong-Bin Zhuang
    Chaire de Simulation à l'Echelle Atomique (CSEA), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.
  • Chang Liu
    Key Lab of Cell Differentiation and Apoptosis of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Jia-Xin Zhu
    State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P.R. China.
  • Jin-Yuan Hu
    State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry & Chemical Engineering, Xiamen University, Xiamen, 361005, China.
  • Jia-Bo Le
    State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry & Chemical Engineering, Xiamen University, Xiamen, 361005, China.
  • Jie-Qiong Li
    State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry & Chemical Engineering, Xiamen University, Xiamen, 361005, China.
  • Xiao-Jian Wen
    State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry & Chemical Engineering, Xiamen University, Xiamen, 361005, China.
  • Xue-Ting Fan
    State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry & Chemical Engineering, Xiamen University, Xiamen, 361005, China.
  • Mei Jia
    Department of Clinical Laboratory, Peking University People's Hospital, Beijing 100044, China. Electronic address: jiameipku@163.com.
  • Xiang-Ying Li
    State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry & Chemical Engineering, Xiamen University, Xiamen, 361005, China.
  • Ao Chen
    BGI Research, Shenzhen 518083, China.
  • Lang Li
    Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA.
  • Zhi-Li Lin
    Laboratory of AI for Electrochemistry (AI4EC), IKKEM, Xiamen, 361005, China.
  • Wei-Hong Xu
    Laboratory of AI for Electrochemistry (AI4EC), IKKEM, Xiamen, 361005, China.
  • Jun Cheng
    School of Electrical and Information Technology, Yunnan Minzu University, Kunming, Yunnan 650500, PR China. Electronic address: jcheng6819@126.com.

Keywords

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