Our work on Bayesian optimization with faux-data injection for target material discovery is presented in Faux-Data Injection Optimization for Accelerating Data-Driven Discovery of Materials
It makes use of faux-data injection in Bayesian optimization loop as illustrated in the figure below:
This code submits SLURM jobs to run propritary DFT software, VASP. A seperate run.bash must be provided based on your VASP's location, HPC's configuration and other parameters. A sample run.bash file is provided which can be replaced based on your setup.
This code also makes use of MongoDB to store values from Bayesian optimization, a MongoDB URI can be provided in defaults.yaml as mongodb_uri:. You can add your URI e.g username:password@cluster0.abcdefg.mongodb.net. If not provided in defaults.yaml, the user will be prompted to add in the command-line.
Create Conda Environment as follows:
conda create -n fdibo_env python==3.6
conda config --append channels conda-forge
conda activate fdibo_env
Once the virtual environment is active, install the required python packages:
pip install -r requirements.txt
Modified version of Rocketsled library is part of this code so no need to install it.
Copy/paste vasp_potcar folder to home directory ~/vasp_potcar
Copy/replace templates folder to ..python-version/site-packages/fireworks/user_objects/firetasks/templates
gedit ~/.pmgrc.yaml
#Add these lines:
PMG_DEFAULT_FUNCTIONAL: PBE_54
PMG_VASP_PSP_DIR: ~/vasp_potcar/MY_PSP/
The computational experiments can be executed by running the following command in the terminal, replacing (method) with either FDI-BO, TOPK-BO, S-BO or random
python BOang-HT-vasp.py (method)
e.g: python BOang-HT-vasp.py FDI-BO
To run with no-hangup:
python BOang-HT-vasp.py (method) </dev/null> name.log 2>1 & disown -h "$!"
Please cite this work as:
@article{ziaullah2023faux,
title={Faux-Data Injection Optimization for Accelerating Data-Driven Discovery of Materials},
author={Ziaullah, Abdul Wahab and Chawla, Sanjay and El-Mellouhi, Fedwa},
journal={Integrating Materials and Manufacturing Innovation},
pages={1--14},
year={2023},
publisher={Springer}
}