DynamicEmbedding

This repository contains the code for Dynamic Embedding framework to predict materials properties. The code provides a pre-trained ALIGNN model to perform dynamic extraction on a given target dataset.

Installation Requirements

The basic requirement for using the files are a Python 3.8 with the packages listed in setup.py. It is advisable to create an virtual environment with the correct dependencies. Please refer to the guidelines here for installation details.

The work related experiments was performed on Linux Fedora 7.9 Maipo. The code should be able to work on other Operating Systems as well but it has not been tested elsewhere.

Source Files

Here is a brief description about the folder content:

• EmbeddingExtractor: code to perform embedding extraction.

• GNN: code to perform model training using GNN that takes dynamic embedding as input.

ALIGNNTL: Fine-Tuning

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.

DynamicEmbedding: Embedding Extraction

The user requires following files in order to perform embedding 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 level encodins one-by-one (batch-wise conversion has not been implemented yet). Example: abc.vasp will produce abc_1.csv to abc_9.csv.

Once you have converted all the structure files in the Input-Property file id_prop.csv using the script file, use Roost here for model training.

Using Pre-Trained Model

Pre-trained models are available at Zenodo, and these models can be used to extract dynamic embedding directly.

To perform prediction using the original ALIGNN model, please refer to https://github.com/usnistgov/alignn

To perform prediction using the original Roost model, please refer to https://github.com/CompRhys/roost

Developer Team

The code was developed by Vishu Gupta from the CUCIS group at the Electrical and Computer Engineering Department at Northwestern University.

Publication

Acknowledgements

The open-source implementation of ALIGNN here provided significant initial inspiration for the structure of this code base.

Disclaimer

The research code shared in this repository is shared without any support or guarantee of its quality. However, please do raise an issue if you find anything wrong, and I will try my best to address it.

email: [email protected]

Copyright (C) 2024, Northwestern University.

See COPYRIGHT notice in top-level directory.

Funding Support

This work was performed under the following financial assistance award 70NANB19H005 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD). Partial support is also acknowledged from NSF award CMMI-2053929, and DOE awards DE-SC0019358, DE-SC0021399, and Northwestern Center for Nanocombinatorics.