bdweb

This is the source code for the BatteryData web app based on Django.

To test this web app locally, install the requirements.txt file and make migrations using the following command:

python manage.py makemigrations
python manage.py migrate

Then run the server:

python manage.py runserver

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

BatteryDataExtractor: battery-aware text-mining software embedded with BERT models

@article{huang2022batterydataextractor,
  title={BatteryDataExtractor: battery-aware text-mining software embedded with BERT models},
  author={Huang, Shu and Cole, Jacqueline M},
  journal={Chemical Science},
  volume={13},
  number={39},
  pages={11487--11495},
  year={2022},
  publisher={Royal Society of Chemistry}
}

BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement

@article{huang2022batterybert,
  title={BatteryBERT: A Pretrained Language Model for Battery Database Enhancement},
  author={Huang, Shu and Cole, Jacqueline M},
  journal={J. Chem. Inf. Model.},
  year={2022},
  doi={10.1021/acs.jcim.2c00035},
  url={DOI:10.1021/acs.jcim.2c00035},
  pages={DOI: 10.1021/acs.jcim.2c00035},
  publisher={ACS Publications}
}

A database of battery materials auto-generated using ChemDataExtractor

@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}
}