Volume 9 Issue 5
May  2024
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Article Navigation >  Green Energy&Environment  > 2024  >  9(5): 877-889
Bin Ma, Lisheng Zhang, Wentao Wang, Hanqing Yu, Xianbin Yang, Siyan Chen, Huizhi Wang, Xinhua Liu. Application of deep learning for informatics aided design of electrode materials in metal-ion batteries. Green Energy&Environment, 2024, 9(5): 877-889. doi: 10.1016/j.gee.2022.10.002
Citation: Bin Ma, Lisheng Zhang, Wentao Wang, Hanqing Yu, Xianbin Yang, Siyan Chen, Huizhi Wang, Xinhua Liu. Application of deep learning for informatics aided design of electrode materials in metal-ion batteries. Green Energy&Environment, 2024, 9(5): 877-889. doi: 10.1016/j.gee.2022.10.002
shu

Application of deep learning for informatics aided design of electrode materials in metal-ion batteries

doi: 10.1016/j.gee.2022.10.002

  • a. School of Transportation Science and Engineering, Beihang University, Beijing, 102206, China;

  • b. College of Automotive Engineering, Jilin University, Changchun, 130022, China;

  • c. Department of Mechanical Engineering, Imperial College London, London, SW7 2BX, UK

Funds:

This work was financially supported by the National Natural Science Foundation of China (No. 52102470).

  • Received date 09 August 2022, Accepted date 11 October 2022, RevRecd date 29 September 2022, Publish date 13 October 2022,
    Abstract
  • To develop emerging electrode materials and improve the performances of batteries, the machine learning techniques can provide insights to discover, design and develop battery new materials in high-throughput way. In this paper, two deep learning models are developed and trained with two feature groups extracted from the Materials Project datasets to predict the battery electrochemical performances including average voltage, specific capacity and specific energy. The deep learning models are trained with the multilayer perceptron as the core. The Bayesian optimization and Monte Carlo methods are applied to improve the prediction accuracy of models. Based on 10 types of ion batteries, the correlation coefficients are maintained above 0.9 compared to DFT calculation results and the mean absolute error of the prediction results for voltages of two models can reach 0.41 V and 0.20 V, respectively. The electrochemical performance prediction times for the two trained models on thousands of batteries are only 72.9 ms and 75.7 ms. Besides, the two deep learning models are applied to approach the screening of emerging electrode materials for sodium-ion and potassium-ion batteries. This work can contribute to a high-throughput computational method to accelerate the rational and fast materials discovery and design.

     

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