Data-driven development of cycle prediction models for lithium metal batteries using multi modal mining

Jaewoong Lee, Junhee Woo, Sejin Kim, Cinthya Paulina, Hyunmin Park +3 more
11/26/2024
cs.LG

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Abstract

Recent advances in data-driven research have shown great potential in understanding the intricate relationships between materials and their performances. Herein, we introduce a novel multi modal data-driven approach employing an Automatic Battery data Collector (ABC) that integrates a large language model (LLM) with an automatic graph mining tool, Material Graph Digitizer (MatGD). This platform enables state-of-the-art accurate extraction of battery material data and cyclability performance metrics from diverse textual and graphical data sources. From the database derived through the ABC platform, we developed machine learning models that can accurately predict the capacity and stability of lithium metal batteries, which is the first-ever model developed to achieve such predictions. Our models were also experimentally validated, confirming practical applicability and reliability of our data-driven approach.

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Cite this paper

@article{lee2024datadriven,
  title  = {Data-driven development of cycle prediction models for lithium metal batteries using multi modal mining},
  author = {Jaewoong Lee and Junhee Woo and Sejin Kim and Cinthya Paulina and Hyunmin Park and Hee-Tak Kim and Steve Park and Jihan Kim},
  year   = {2024},
  eprint = {2411.17625},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2411.17625v1}
}

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