This is the official Implementation of the DeepDFT model for charge density prediction.

Create and activate a virtual environment and install the requirements:

$ pip install -r requirements.txt

Training data is expected to be a tar file containing .cube (Gaussian) or .CHGCAR (VASP) density files. For best performance the tar files should not be compressed, but the individual files inside the tar can use zlib compression (add .zz extension) or lz4 compression (add .lz4 extension). The data can be split up in several tar files. In that case create a text (.txt) file in the same directory as the tar files. The text file must contain the file names of the tar files, one on each line. Then the text file can then be used as a dataset.

Inspect runner.py arguments:

$ python runner.py --help

Example used for training the model on QM9:

$ python runner.py --dataset datadir/qm9vasp.txt --split_file datadir/splits.json --ignore_pbc --cutoff 4 --num_interactions 6 --max_steps 100000000 --node_size 128

The json file contains two keys "train", and "validation" each with a list of indices for the train and validation sets. If the argument is omitted the data will be randomly split.

To use a trained model to predict the electron density around a new structure use the script predict_with_model.py. The first argument is the output directory of the runner script in which the trained model is saved. The second argument is an ASE compatible xyz file with atom coordinates for the structure to be predicted.

For example:

$ python predict_with_model.py qm9_pretrained_model example_molecule.xyz

For more options see the predict_with_model.py optional arguments:

$ python predict_with_model.py --help