FeatureExtraction

ALIGNNTL: Feature Extraction

This directory contains information on how to perform feature extraction using ALIGNN.

Instructions

The user requires following files in order to perform feature extraction

• Sturcture files - contains structure information for a given material (format: POSCAR, .cif, .xyz or .pdb)
• Input-Property file - contains name of the structure file and its corresponding property value (format: .csv)
• Pre-trained model - model trained using ALIGNN using any specific materials property (format: .zip)

We have provided the an example of Sturcture files (POSCAR files) and Input-Property file (id_prop.csv) in examples. Download the pre-trained model trained on large datasets from here.

Now, in order to perform feature extraction, add the details regarding the model in the all_models dictionary inside the train.py file as described below:

all_models = {
    name of the file: [link to the pre-trained model (optional), number of outputs],
    name of the file 2: [link to the pre-trained model 2 (optional), number of outputs],
    ...
    }

If the link to the pre-trained model is not provided inside the all_models dictionary, place the zip file of the pre-trained model inside the alignn folder. Once the setup for the pre-trained model is done, the feature extraction can be performed by running the create_features.sh script file which contains the following code:

for filename in ../examples/*.vasp; do
    python alignn/pretrained_activation.py --model_name mp_e_form_alignnn --file_format poscar --file_path "$filename" --output_path "../examples/data"
done

The script will convert the structure files into atom (x), bond (y) and angle (z) based features one-by-one (batch-wise conversion has not been implemented yet). Example: abc.vasp will produce abc_x.csv (9 atom-based features), abc_y.csv (9 bond-based features) and abc_z.csv (5 angle-based features).

Once you have converted all the structure files in the Input-Property file id_prop.csv using the script file, run the jupyter notebooks pre-processing.ipynb to convert the structure-wise features into a dataset. Pre-processing steps contained within the pre-processing.ipynb file is as follows:

• Attach the appropriate property value and identifier (jid) to each of the extracted features file based on id_prop.csv
• Create a seperate file for each of the features (atom, bond, angle) based on the extracted checkpoints
• Create combined features (in the order of atom, bond and angle) from same (3-1) or different (3-2) checkpoints. Use first 512 features for atom+bond and all features for atom+bon+angle as input for model training.
• (Optional) Divide each of the files into train, validation and test files based on the json file ids_train_val_test.json available in the output directory of the ALIGNN model