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: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2052-4463 |