| FUNDAMENTAL AREAS OF PHENOMENOLOGY(INCLUDING APPLICATIONS) |
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ESM Cloud Toolkit: A Copilot for Energy Storage Material Research |
| Jing Xu1,2, Ruijuan Xiao1,2*, and Hong Li1,2 |
1Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
2School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
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| Cite this article: |
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Jing Xu, Ruijuan Xiao, and Hong Li 2024 Chin. Phys. Lett. 41 054701 |
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Abstract Searching and designing new materials play crucial roles in the development of energy storage devices. In today's world where machine learning technology has shown strong predictive ability for various tasks, the combination with machine learning technology will accelerate the process of material development. Herein, we develop ESM Cloud Toolkit for energy storage materials based on MatElab platform, which is designed as a convenient and accurate way to automatically record and save the raw data of scientific research. The ESM Cloud Toolkit includes multiple features such as automatic archiving of computational simulation data, post-processing of experimental data, and machine learning applications. It makes the entire research workflow more automated and reduces the entry barrier for the application of machine learning technology in the domain of energy storage materials. It integrates data archive, traceability, processing, and reutilization, and allows individual research data to play a greater role in the era of AI.
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Received: 03 January 2024
Published: 28 May 2024
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| PACS: |
47.54.Jk
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(Materials science applications)
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63.20.dk
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(First-principles theory)
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07.05.Hd
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(Data acquisition: hardware and software)
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07.05.Mh
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(Neural networks, fuzzy logic, artificial intelligence)
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