Auto-MISCHBARES, building upon our HELAO framework, is designed for high-throughput electrochemical research. It automates the study of different electrolyte and/or electrode materials, different electrochemical protocols in order to characterize the interphase formations at a millimeter scale, enhancing the efficiency of material discovery. This system's significant feature is its ability to autonomously asynchronously orchestrate sequential or parallel experiments, integrated with advanced Quality Control assessments and MADAP for advanced data analysis using AI algorithms. The web interface of Auto-MISCHBARES offers streamlined user control, and its database design adheres to FAIR principles, promoting robust and transparent research in battery material science.
- Python 3.8
- PostgreSQL
- Libraries listed in requirements.txt
Clone the repository:
git clone https://github.com/fuzhanrahmanian/MISCHBARES.git
Navigate to the directory:
cd MISCHBARES
Install the required libraries:
pip install -r requirements.txt
Run the application:
python app.py
Navigate to the db directory:
cd db
Initialize the PostgreSQL database using the schema file:
psql -U [username] -d [database_name] -a -f mischbares_db.sql
Replace [username] and [database_name] with your PostgreSQL credentials.
Tutorial and demonstration can be find at https://doi.org/10.5281/zenodo.10445749.
The data related to this study is available at https://doi.org/10.5281/zenodo.10444324.
If you use this software in your research, please cite the following paper: preprint
For more detailed information, please visit the documentation page