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 ...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 Mat Elab 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.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos. 52022106 and 52172258)the Informatization Plan of Chinese Academy of Sciences (Grant No. CASWX2021SF-0102)。
文摘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 Mat Elab 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.
