  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.
