matten

This repo contains the Materials Tensor (MatTen) model for predicting tensorial properties of crystals such as the elasticity tensor.

MatTen is an equivariant graph neural network built using e3nn.

Install

Follow the official documentation to install pytorch>=2.0.0. Then

git clone https://github.com/wengroup/matten.git
pip install -e ./matten

If you get package version conflicts, try the below command to install the dependencies with strict version requirements.

pip install -e "./matten[strict]"

Use the pretrained model

from pymatgen.core import Structure
from matten.predict import predict


def get_structure():
    a = 5.46
    lattice = [[0, a / 2, a / 2], [a / 2, 0, a / 2], [a / 2, a / 2, 0]]
    basis = [[0.0, 0.0, 0.0], [0.25, 0.25, 0.25]]
    Si = Structure(lattice, ["Si", "Si"], basis)

    return Si


structure = get_structure()

elasticity_tensor = predict(structure)

The predict function returns an elasticity tensor. To make predictions for multiple crystals, pass a list of structures to predict.

Data

• An example of 100 crystals is available in the datasets directory.
• The full dataset is available at: https://doi.org/10.5281/zenodo.8190849

Train the model (using your own data)

See instructions here.

Reference

Wen, M., Horton, M. K., Munro, J. M., Huck, P., & Persson, K. A. (2024). An equivariant graph neural network for the elasticity tensors of all seven crystal systems. Digital Discovery, 3(5), 869–882. doi: 10.1039/D3DD00233K

@article{matten,
  author = {Wen, Mingjian and Horton, Matthew K. and Munro, Jason M. and Huck, Patrick and Persson, Kristin A.},
  title = {An equivariant graph neural network for the elasticity tensors of all seven crystal systems},
  journal = {Digital Discovery},
  volume = {3},
  number = {5},
  pages = {869--882},
  year = {2024},
  publisher = {RSC},
  doi = {10.1039/D3DD00233K}
}