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.