Implemens the Differentiable Optimization for the Prediction of Ground State Structures (DOGSS) that takes arbitrary chemical structures to predict their ground-state structures. The following paper describes the details of the DOGSS framework: Differentiable Optimization for the Prediction of Ground State Structures (DOGSS)
Create conda envionrment with require packages:
conda env create -f env.yml
Activate the conda environment with
conda activate dogss
Install the package with pip install -e .
.
We provide scripts to train/load DOGSS for predicting ground state structures of only H adsorption
dataset. Other datasets mentioned in the paper (Bare surfaces
and CO adsorption
) can be used in the same way but with different hyperparameters.