Open-Catalyst-Project Models

Implements the following baselines that take arbitrary chemical structures as input to predict material properties:

Installation

[last updated October 10, 2020]

The easiest way of installing prerequisites is via conda. After installing conda, run the following commands to create a new environment named ocp-models and install dependencies:

Pre-install step

Install conda-merge:

pip install conda-merge

If you're using system pip, then you may want to add the --user flag to avoid using sudo. Check that you can invoke conda-merge by running conda-merge -h.

GPU machines

Instructions are for PyTorch 1.6, CUDA 10.1 specifically.

First, check that CUDA is in your PATH and LD_LIBRARY_PATH, e.g.

$echo $PATH | tr ':' '\n' | grep cuda
/public/apps/cuda/10.1/bin
$echo $LD_LIBRARY_PATH | tr ':' '\n' | grep cuda
/public/apps/cuda/10.1/lib64

The exact paths may differ on your system. Then install the dependencies:

conda-merge env.common.yml env.gpu.yml > env.yml
conda env create -f env.yml

Activate the conda environment with conda activate ocp-models. Install this package with pip install -e .. Finally, install the pre-commit hooks:

pre-commit install

CPU-only machines

Please skip the following if you completed the with-GPU installation from above.

conda-merge env.common.yml env.cpu.yml > env.yml
conda env create -f env.yml
conda activate ocp-models
pip install -e .
pre-commit install

Usage

Project website

The project website is opencatalystproject.org. Links to dataset paper and the whitepaper can be found on the website.

Download the datasets

Dataset download links can be found at DATASET.md for the S2EF, IS2RS, and IS2RE tasks. IS2* datasets are stored as LMDB files and are ready to be used upon download. S2EF datasets require an additional preprocessing step.

Preprocess datasets - S2EF only

Download the dataset of interest: curl -O download_link
Untar the dataset tar -xvf dataset_name.tar
Uncompress the untarred directory contents: python ocp/scripts/uncompress.py --ipdir /path/to/dataset_name_compressed --opdir raw_data/
Run the LMDB preprocessing script: python ocp/scripts/preprocess_ef.py --data-path raw_data/ --out-path processed_lmdb/ --num-workers 32 --get-edges --ref-energy; where
- --get-edges: includes edge information in LMDBs (~10x storage requirement, ~3-5x slowdown), otherwise, compute edges on the fly (larger GPU memory requirement).
- --ref-energy: uses referenced energies instead of raw energies.

Train models for the desired tasks

A detailed description of how to train, predict, and run ML-based relaxations can be found here.

Pretrained models

Pretrained models accompanying https://arxiv.org/abs/2010.09990v1 can be found here.

Acknowledgements

This codebase was initially forked from CGCNN by Tian Xie, but has undergone significant changes since.
A lot of engineering ideas have been borrowed from github.com/facebookresearch/mmf.
The DimeNet++ implementation is based on the author's Tensorflow implementation and the DimeNet implementation in Pytorch Geometric.

License

This code is MIT licensed, as found in the LICENSE file.

borao/ocp