Utilizing Deep Learning for Crystal System Classification in Lithium - Ion Batteries

Authors

  • Yibo Yin Computer Science, Contemporary Amperex Technology USA Inc, Auburn Hills, USA
  • Guokun Xu Computer Science, Beijing Foreign Studies University, Beijing, China
  • Ying Xie Computer Science, San Francisco Bay University, Fremont, USA
  • Yang Luo Computer Science, China CITIC Bank Software Development Center, Beijing, China
  • Zibu Wei Computer Science, University of California, Los Angeles, Los Angeles, USA
  • Zhengning Li Computer Science, Georgetown University, Washington, D.C. USA

DOI:

https://doi.org/10.53469/jtpes.2024.04(03).19

Keywords:

Lithium-ion batteries, Crystal system properties, Deep Neural Networks

Abstract

Lithium-ion (Li-ion) batteries are pivotal in energy storage, powering diverse applications from portable electronics to electric vehicles. Optimizing Li-ion battery performance relies on understanding the crystal system properties of constituent materials, notably cathodes. This paper proposes a novel approach using Deep Neural Networks (DNNs) for multi-class classification of Li-ion silicate cathode crystal systems. Previous research underscores crystal chemistry's importance and the potential of machine learning in Li-ion battery materials. However, existing methodologies face challenges in accurately capturing material complexities. Our DNN-based model aims to address these limitations, offering improved predictive performance for crystal system classification.

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Published

2024-03-26

How to Cite

Yin, Y., Xu, G., Xie, Y., Luo, Y., Wei, Z., & Li, Z. (2024). Utilizing Deep Learning for Crystal System Classification in Lithium - Ion Batteries. Journal of Theory and Practice of Engineering Science, 4(03), 199–206. https://doi.org/10.53469/jtpes.2024.04(03).19

Issue

Vol. 4 No. 03 (2024): JTPES-2023-4-3

Section

Articles

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Copyright (c) 2024 Yibo Yin, Guokun Xu, Ying Xie, Yang Luo, Zibu Wei, Zhengning Li

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.