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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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05338-5 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Yong-Bin Zhuang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-af771a6d65e94513a451ff77dc051bed |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-af771a6d65e94513a451ff77dc051bed2025-08-20T03:45:11ZengNature PortfolioScientific Data2052-44632025-06-011211910.1038/s41597-025-05338-5An artificial intelligence accelerated ab initio molecular dynamics dataset for electrochemical interfacesYong-Bin Zhuang0Chang Liu1Jia-Xin Zhu2Jin-Yuan Hu3Jia-Bo Le4Jie-Qiong Li5Xiao-Jian Wen6Xue-Ting Fan7Mei Jia8Xiang-Ying Li9Ao Chen10Lang Li11Zhi-Li Lin12Wei-Hong Xu13Jun Cheng14State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry & Chemical Engineering, Xiamen UniversityState Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry & Chemical Engineering, Xiamen UniversityState Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry & Chemical Engineering, Xiamen UniversityState Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry & Chemical Engineering, Xiamen UniversityState Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry & Chemical Engineering, Xiamen UniversityState Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry & Chemical Engineering, Xiamen UniversityState Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry & Chemical Engineering, Xiamen UniversityState Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry & Chemical Engineering, Xiamen UniversityState Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry & Chemical Engineering, Xiamen UniversityState Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry & Chemical Engineering, Xiamen UniversityState Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry & Chemical Engineering, Xiamen UniversityState Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry & Chemical Engineering, Xiamen UniversityLaboratory of AI for Electrochemistry (AI4EC), IKKEMLaboratory of AI for Electrochemistry (AI4EC), IKKEMState Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, Department of Chemistry, College of Chemistry & Chemical Engineering, Xiamen UniversityAbstract 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.https://doi.org/10.1038/s41597-025-05338-5 |
| spellingShingle | 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 An artificial intelligence accelerated ab initio molecular dynamics dataset for electrochemical interfaces Scientific Data |
| title | An artificial intelligence accelerated ab initio molecular dynamics dataset for electrochemical interfaces |
| title_full | An artificial intelligence accelerated ab initio molecular dynamics dataset for electrochemical interfaces |
| title_fullStr | An artificial intelligence accelerated ab initio molecular dynamics dataset for electrochemical interfaces |
| title_full_unstemmed | An artificial intelligence accelerated ab initio molecular dynamics dataset for electrochemical interfaces |
| title_short | An artificial intelligence accelerated ab initio molecular dynamics dataset for electrochemical interfaces |
| title_sort | artificial intelligence accelerated ab initio molecular dynamics dataset for electrochemical interfaces |
| url | https://doi.org/10.1038/s41597-025-05338-5 |
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