batterydatabase

Tools for auto-generating the battery materials database.

Installation

Please first install the public ChemDataExtractor version (v1.3):

conda install -c chemdataextractor chemdataextractor

Download the necessary data files (machine learning models, dictionaries, etc.):

cde data download

Then install the dependency packages for the bespoke version for batteries (chemdataextractor_batteries v1.5):

pip install -r requirements.txt

Usage

To extract raw data from text, you need to provide the root of the paper folder, output root to data record folder, start and end index of papers, and the file name to be saved.

For example, extract the first paper of test/ and save to save/ as raw_data.json:

python extract.py --input_dir test/ --output_dir save/ --start 0 --end 1 --save_name raw_data

After the raw data is extracted, it needs to be cleaned and converted into a standard format. We provide the data cleaning code in dataclean.ipynb. The final data format can be .json, .csv or .db.

Acknowledgements

This project was financially supported by the Science and Technology Facilities Council (STFC), the Royal Academy of Engineering (RCSRF1819\7\10) and Christ's College, Cambridge. The Argonne Leadership Computing Facility, which is a DOE Office of Science Facility, is also acknowledged for use of its research resources, under contract No. DEAC02-06CH11357.

Citation

@article{huang2020database,
  title={A database of battery materials auto-generated using ChemDataExtractor},
  author={Huang, Shu and Cole, Jacqueline M},
  journal={Scientific Data},
  volume={7},
  number={1},
  pages={1--13},
  year={2020},
  publisher={Nature Publishing Group}
}