FineTuning

ALIGNNTL: Fine-Tuning

This directory contains information on how to perform fine-tuning using ALIGNN.

Instructions

The user requires following files in order to start training a model using fine-tuning method

• 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)
• Configuration file - configuration file with hyperparamters associated with training the model (format: .json)
• 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), Input-Property file (id_prop.csv) and Configuration file (config_example.json) in examples. Download the pre-trained model trained on large datasets from here.

Now, in order to perform fine-tuning based transfer learning, 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 fine-tuning based model training can be performed as follows:

python alignn/train_folder.py --root_dir "../examples" --config "../examples/config_example.json" --id_prop_file "id_prop.csv" --output_dir=model

Make sure that the Input-Property file --id_prop_file is placed inside the root directory --root_dir where Sturcture files are present.