mxtaltools 0.1.0

MXtalTools: Molecular Crystals Machine Learning Toolkit

MXtalTools: Toolbox for machine learning on molecular crystals

Documentation

See our detailed documentation including installation and deployment instructions at our readthedocs page.

Installation for Users

1. Install PyTorch, Pytorch Geometric (including torch-scatter, torch-sparse, torch-cluster), based on your system and CUDA version:
   PyTorch installation guide
   PyG installation guide

2. Install this package:

   pip install git+https://github.com/InfluenceFunctional/MXtalTools.git
   

Installation for Developers

1. Download the code from this repository via

   git clone [e-mail]:InfluenceFunctional/MXtalTools.git MXtalTools
   

2. Create a python environment of your choice. We recommend using pip+virtualenv.

3. Install PyTorch, Pytorch Geometric (including torch-scatter, torch-sparse, torch-cluster), based on your system and CUDA version:
   PyTorch installation guide
   PyG installation guide

4. Install remaining requirements with

   poetry install
   

5. If you plan to train any models, login to your weights and biases ("wandb") account, which is necessary for run monitoring and reporting with

    wandb login
   

6. In configs/users create a .yaml file for yourself and edit the paths and wandb details to correspond to your preferences. When running the code, append the following to your command line prompt.

    --user YOUR_USERNAME
   

7. If you plan to construct crystal datasets from .cif files, you'll need to install the CSD python api, with a valid license from CCDC.

   [CSD Python API](PyTorch installation guide)

Reference

If you use this code in any future publications, please cite our work using

  title={Geometric deep learning for molecular crystal structure prediction},
  author={Kilgour, Michael and Rogal, Jutta and Tuckerman, Mark},
  journal={Journal of chemical theory and computation},
  volume={19},
  number={14},
  pages={4743--4756},
  year={2023},
  publisher={American Chemical Society}
}