Summary of Data-driven Development Of Cycle Prediction Models For Lithium Metal Batteries Using Multi Modal Mining, by Jaewoong Lee et al.
Data-driven development of cycle prediction models for lithium metal batteries using multi modal mining
by Jaewoong Lee, Junhee Woo, Sejin Kim, Cinthya Paulina, Hyunmin Park, Hee-Tak Kim, Steve Park, Jihan Kim
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel multi-modal data-driven approach has been introduced for understanding material-performance relationships. The Automatic Battery data Collector (ABC) integrates a large language model with an automatic graph mining tool, Material Graph Digitizer (MatGD). This platform enables accurate extraction of battery material data and cyclability performance metrics from diverse textual and graphical sources. Machine learning models were developed to predict the capacity and stability of lithium metal batteries, achieving state-of-the-art accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way has been found to understand how different materials work together. It uses a special tool that collects lots of information about battery materials and their performance. This tool combines two powerful machines: one that understands language and another that reads graphs. The tool can take in many types of data, like text and pictures, and turn it into useful information. Scientists have used this tool to create models that can predict how well batteries will work. These predictions are very accurate and show that the new approach is reliable. |
Keywords
» Artificial intelligence » Large language model » Machine learning » Multi modal
