用多模态数据挖掘预测锂金属电池循环性能，首次实现高精度建模。
Data-driven development of cycle prediction models for lithium metal batteries using multi modal mining
- 融合大语言模型与图挖掘工具，自动提取图文数据中的电池材料信息。
- 构建的模型可精准预测锂金属电池容量与稳定性，准确率领先现有方法。
- 适用于电池材料研发人员，加速新型电池体系设计与优化。
数据驱动研究在揭示材料与性能之间复杂关系方面展现出巨大潜力。本文提出一种新型多模态数据驱动方法，通过自动电池数据采集平台（ABC）整合大语言模型（LLM）与自动图挖掘工具Material Graph Digitizer（MatGD），实现从多样化文本与图形数据源中高精度提取电池材料数据及循环性能指标。基于该平台构建的数据库，我们开发了首个能够准确预测锂金属电池容量与稳定性的机器学习模型，并通过实验验证其实际应用价值与可靠性，展示了数据驱动方法在电池研发中的有效性。
原文摘要 · Abstract (English)
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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