Text-to-Battery Recipe: A language modeling-based protocol for automatic battery recipe extraction and retrieval

TL;DR

This paper introduces Text-to-Battery Recipe (T2BR), a novel NLP protocol that automatically extracts detailed battery manufacturing recipes from scientific literature, enabling systematic analysis and accelerating battery research.

Contribution

The study develops a comprehensive NLP-based framework for extracting battery recipes from literature, including paper filtering, topic modeling, and entity recognition, with high accuracy.

Findings

Successfully extracted 165 battery recipes for LiFePO4 batteries.

Achieved high F1 scores of 88.18% and 94.61% in entity recognition.

Identified key trends and associations in battery manufacturing data.

Abstract

Recent studies have increasingly applied natural language processing (NLP) to automatically extract experimental research data from the extensive battery materials literature. Despite the complex process involved in battery manufacturing -- from material synthesis to cell assembly -- there has been no comprehensive study systematically organizing this information. In response, we propose a language modeling-based protocol, Text-to-Battery Recipe (T2BR), for the automatic extraction of end-to-end battery recipes, validated using a case study on batteries containing LiFePO4 cathode material. We report machine learning-based paper filtering models, screening 2,174 relevant papers from the keyword-based search results, and unsupervised topic models to identify 2,876 paragraphs related to cathode synthesis and 2,958 paragraphs related to cell assembly. Then, focusing on the two topics, two deep learning-based named entity recognition models are developed to extract a total of 30 entities -- including precursors, active materials, and synthesis methods -- achieving F1 scores of 88.18% and 94.61%. The accurate extraction of entities enables the systematic generation of 165 end-toend recipes of LiFePO4 batteries. Our protocol and results offer valuable insights into specific trends, such as associations between precursor materials and synthesis methods, or combinations between different precursor materials. We anticipate that our findings will serve as a foundational knowledge base for facilitating battery-recipe information retrieval. The proposed protocol will significantly accelerate the review of battery material literature and catalyze innovations in battery design and development.