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

• URL: http://arxiv.org/abs/2411.17625v1
• Date: Tue, 26 Nov 2024 17:37:12 GMT
• Title: Data-driven development of cycle prediction models for lithium metal batteries using multi modal mining
• Authors: Jaewoong Lee, Junhee Woo, Sejin Kim, Cinthya Paulina, Hyunmin Park, Hee-Tak Kim, Steve Park, Jihan Kim,
• Abstract summary: We introduce a novel multi modal data-driven approach employing an Automatic Battery data Collector (ABC) This platform enables state-of-the-art accurate extraction of battery material data and cyclability performance metrics. 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.
• Score: 1.2748196295556375
• License:
• 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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