PotNet

Official code repository of paper “Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction” by Yuchao Lin, Keqiang Yan, Youzhi Luo, Yi Liu, Xiaoning Qian, and Shuiwang Ji. [ICML 2023 Poster]

A short mathematical sum-up of integral summation can be seen here

Environment Setup

• We'll use conda to install dependencies and set up the environment. We recommend using the Python 3.9 Miniconda installer.
• After installing conda, run

conda env create -f environment.yml

• Then run below code to activate the environment

conda activate potnet

• We are using torch-geometric for the implementation of our graph neural networks. Installation of torch-geometric via conda seems problematic so we install it explicitly using pip as

pip install torch-scatter==2.1.0 torch-sparse==0.6.16 torch-cluster==1.6.0 torch-spline-conv==1.2.1 -f https://data.pyg.org/whl/torch-1.12.1%2Bcu116.html

and

pip install torch-geometric

• Be aware that we are using an old version of JARVIS toolkits jarvis-tools==2022.9.16. The newest JARVIS toolkits will contain new versions of datasets that include more data than the one we present in the paper.

Running Summation Algorithm

• To run the summation algorithm, please run below commands in order to install the algorithm package (remember to replace the TARGET_PATH with your own destination.)

cd functions
tar xzvf gsl-latest.tar.gz
cd gsl-2.7.1
./configure --prefix=TARGET_PATH
make
make install

• Then edit ~/.bashrc by adding

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:TARGET_PATH/lib/

and

source ~/.bashrc

or create another terminal.

• Now we back to functions directory and run

python setup.py build_ext --inplace

• Then the algorithm is installed as Cython package. A simple way to test if it is successfully installed is to run below in the root directory.

python test_algorithm.py

Using Summation Algorithm

In this code base, we provide six infinite summations of

• Coulomb potential
• London dispersion potential
• Pauli repulsion potential
• Lennard-Jones potential
• Morse potential
• Screened Coulomb potential

and they are achieved in algorithm.py by

• epstein referring to summation \sum_{\mathbf{k}\in \mathbb{Z}^d, \Vert \mathbf{L}\mathbf{k}+\mathbf{v} \Vert\ne 0}\frac{e^{2\pi i \mathbf{w} \cdot \mathbf{L} \mathbf{k}}}{\Vert \mathbf{L}\mathbf{k}+\mathbf{v} \Vert^{2p}}
• zeta referring to summation \sum_{\mathbf{k}\in \mathbb{Z}^d, \Vert \mathbf{L}\mathbf{k}+\mathbf{v} \Vert\ne 0}\frac{1}{\Vert \mathbf{L}\mathbf{k}+\mathbf{v} \Vert^{2p}}
• exp referring to summation \sum_{\mathbf{k}\in \mathbb{Z}^d } e^{-\alpha \Vert \mathbf{L}\mathbf{k}+\mathbf{v} \Vert}
• lj referring to summation \sum_{\mathbf{k}\in \mathbb{Z}^d, \Vert \mathbf{L}\mathbf{k}+\mathbf{v} \Vert\ne 0}(\frac{\sigma^{12}}{\Vert \mathbf{L}\mathbf{k}+\mathbf{v} \Vert^{12}} - \frac{\sigma^6}{\Vert \mathbf{L}\mathbf{k}+\mathbf{v} \Vert^6} )
• morse referring to summation \sum_{\mathbf{k}\in \mathbb{Z}^d} (e^{-2\alpha (\Vert \mathbf{L}\mathbf{k}+\mathbf{v} \Vert - r_e) } - 2e^{-\alpha (\Vert \mathbf{L}\mathbf{k}+\mathbf{v} \Vert - r_e)})
• screened_coulomb referring to summation \sum_{\mathbf{k}\in\mathbb{Z}^d, \Vert \mathbf{L}\mathbf{k}+\mathbf{v} \Vert\ne 0} \frac{e^{-\alpha \Vert \mathbf{L}\mathbf{k}+\mathbf{v} \Vert}}{\Vert \mathbf{L}\mathbf{k}+\mathbf{v} \Vert}

Each function requires the input of vectors v and a lattice matrix Omega, alongside a particular param and dimension d. We've also incorporated parameter R, which denotes half of the grid's length, and verbose which, when enabled, conducts corresponding error bound calculations.

For executing a single computation in parallel, set the parallel parameter to True. By default, the program will use NUM_CPUS (set to 32) for concurrent computation. However, keep in mind that during data processing within our model, this setting is overridden to False. This is due to the fact that we've applied a more efficient parallelization method via pandarallel.

The summation is computed over the grid instead of the ellipsoid, as we found this is much more efficient for computing summations of multiple vectors by numpy vectorization. Also, currently, we have implemented the error bound computation for epstein, zeta, and exp which are used in our model. For details of error bound computation, please refer to Appendix C.5 in our paper.

Train and Evaluate Models

In this code base, the datasets are directly provided by JARVIS toolkits and there is no need to download the JARVIS or Materials Project dataset from the official site. To change between different datasets and among different properties, go to potnet.yaml and set the corresponding entries such as

dataset: dft_3d
# JARVIS dataset: dft_3d
# MP dataset: megnet
target: formation_energy_peratom
# JARVIS dataset entries: formation_energy_peratom, mbj_bandgap, optb88vdw_bandgap, optb88vdw_total_energy, ehull
# MP dataset entries: e_form, gap pbe

To train our model, use the script

python main.py --config configs/potnet.yaml --output_dir xxx --checkpoint xxx

Here, output_dir denotes the output directory of checkpoints and processed data files, and checkpoint denotes the path of a checkpoint meaning restarting training from a certain checkpoint. One can also omit checkpoint in this script. Note that after training, the code will conduct the evaluation for the last epoch. It is recommended to do an evaluation below by specifying a checkpoint from the best 5 saved in the checkpoint directory.

To evaluate our model, use the script

python main.py --config configs/potnet.yaml --output_dir xxx --checkpoint xxx --testing

and here checkpoint denotes the path of a checkpoint and testing denotes enabling the evaluation phase.

Train on Custom Dataset

We are supporting custom datasets in the same format as datasets in JARVIS Leaderboard [4]. Once the corresponding dataset is prepared, use the script

python main.py --config configs/potnet.yaml --output_dir xxx --checkpoint xxx --data_root xxx

and change the target name as target in potnet.yaml such like

dataset: dft_3d
target: target

And here data_root denotes the path of the custom dataset, where dataset information dataset_info.json and targets id_prop.csv are included. Then our code will read the dataset information from dataset_info.json and id_prop.csv in the dataset directory and then read the data from data_root. Note that a dataset in the JARVIS Leaderboard can be generated by jarvis_populate_data.py. To generate the same dataset format based on jarvis_populate_data.py to accommodate our code, it is recommended to

• Generate the crystal structures that can be read by JARVIS toolkits and their corresponding properties to predict
• Predefine the train-val-test split of your dataset and write the crystal ids and targets in order in id_prop.csv

Pretrained Models

We provide preprocessed files and pretrained models in this google drive. Right now we only provide the checkpoint for formation energy per atom of the JARVIS dataset. Please stay tuned for more pretrained models! To use these files, specify --output_dir such like

python main.py --config configs/potnet.yaml --output_dir formation_energy_peratom --checkpoint xxx

then the processed file will be read automatically.

Acknowledgement

The underlying training part is based on ALIGNN [2] and the incomplete Bessel Function is based on ScaFaCoS [3]. This work was supported in part by National Science Foundation grants IIS-1908220, CCF-1553281, IIS-1812641, DMR-2119103, and IIS-2212419, and National Institutes of Health grant U01AG070112.

Reference

[1] Crandall, R. E. (1998). Fast evaluation of Epstein zeta functions.

[2] Choudhary, K. and DeCost, B. (2021). Atomistic line graph neural network for improved materials property predictions. npj Computational Materials, 7(1), p.185.

[3] Sutmann, G. (2014). ScaFaCoS–A Scalable library of Fast Coulomb Solvers for particle Systems.

[4] Choudhary, K., et al. (2023). Large Scale Benchmark of Materials Design Methods.

License

We are using the same license GPL-3.0 as the one used in ScaFaCoS [3].

Citation

@inproceedings{lin2023efficient,
	author = {Yuchao Lin and Keqiang Yan and Youzhi Luo and Yi Liu and Xiaoning Qian and Shuiwang Ji},
	title = {Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction},
	booktitle = {Proceedings of the 40th International Conference on Machine Learning},
	year = {2023},
}