This is the VSSR-MC algorithm for sampling surface reconstructions. VSSR-MC samples across both compositional and configurational spaces. It can interface with both a neural network potential (through ASE) or a classical potential (through ASE or LAMMPS). It is a key component of the Automatic Surface Reconstruction (AutoSurfRecon) pipeline described in the following work: Machine-learning-accelerated simulations to enable automatic surface reconstruction.
We recommend a computer with the following specs:
- RAM: 16+ GB
- CPU: 4+ cores, 3 GHz/core
To run with a neural network force field, a GPU is recommended. We ran on a single NVIDIA GeForce RTX 2080 Ti 11 GB GPU. The code has been tested on Linux Ubuntu 20.04.6 LTS but we expect it to work on other Linux distributions.
To start, run git clone [email protected]:learningmatter-mit/surface-sampling.git to your local directory or a workstation.
We recommend creating a new Conda environment. Following that, the Python dependencies for the code can be installed. In the surface-sampling directory, run the following commands:
conda create -n vssr-mc python=3.11
conda activate vssr-mc
conda install -c conda-forge kimpy lammps openkim-models
pip install -e .If you're intending to contribute to the code, you can
pip install -e '.[dev]'instead to also install the development dependencies.
To run with LAMMPS, add the following to ~/.bashrc or equivalent with appropriate paths and then source ~/.bashrc. conda would have installed LAMMPS as a dependency.
export LAMMPS_COMMAND="/path/to/lammps/src/lmp"
export LAMMPS_POTENTIALS="/path/to/lammps/potentials/"
export ASE_LAMMPSRUN_COMMAND="$LAMMPS_COMMAND"The LAMMPS_COMMAND should point to the LAMMPS executable, which can be found here: /path/to/[vssr-mc-env]/bin/lmp.
The LAMMPS_POTENTIALS directory should contain the LAMMPS potential files, which can found here: /path/to/[surface-sampling-repo]/mcmc/potentials/.
The ASE_LAMMPSRUN_COMMAND should point to the same LAMMPS executable. More information can be found here: ASE LAMMPS.
If the conda installed LAMMPS does not work, you might have to install LAMMPS from source. More information can be found here: LAMMPS.
You might have to re-open/re-login to your terminal shell for the new settings to take effect.
A toy demo and other examples can be found in the tutorials/ folder.
tutorials/
├── example.ipynb
├── GaN_0001.ipynb
├── Si_111_5x5.ipynb
├── SrTiO3_001.ipynb
├── latent_space_clustering.ipynb
└── tutorials/prepare_surface.ipynb
More data/examples can be found in our Zenodo dataset.
A toy example to illustrate the use of VSSR-MC. It should only take about a few seconds to run. Refer to tutorials/example.ipynb.
This example could take a few minutes to run. Refer to tutorials/GaN_0001.ipynb.
This example could take a few minutes to run. Refer to tutorials/Si_111_5x5.ipynb.
Demonstrates the integration of VSSR-MC with a neural network force field. This example could take a few minutes to run. Refer to tutorials/SrTiO3_001.ipynb.
Retrieves the neural network embeddings of VSSR-MC structures and performs clustering. This example should only take a minute to run. Refer to tutorials/latent_space_clustering.ipynb.
This example demonstrates how to cut a surface from a bulk structure. Refer to tutorials/prepare_surface.ipynb.
Scripts can be found in the scripts/ folder, including:
scripts/
├── sample_surface.py
└── clustering.py
The arguments for the scripts can be found by running python scripts/sample_surface.py -h or python scripts/clustering.py -h.
python scripts/sample_surface.py --run_name "SrTiO3_001_painn" \
--starting_structure_path "tutorials/data/SrTiO3_001/SrTiO3_001_2x2_pristine_slab.pkl" \
--model_type "PaiNN" --model_paths "tutorials/data/SrTiO3_001/nff/model01/best_model" \
"tutorials/data/SrTiO3_001/nff/model02/best_model" \
"tutorials/data/SrTiO3_001/nff/model03/best_model" \
--settings_path "scripts/configs/sample_config_painn.json"python scripts/sample_surface.py --run_name "SrTiO3_001_chgnet" \
--starting_structure_path "tutorials/data/SrTiO3_001/SrTiO3_001_2x2_pristine_slab.pkl" \
--model_type "CHGNetNFF" --settings_path "scripts/configs/sample_config_chgnet.json"python scripts/clustering.py --file_paths "tutorials/data/SrTiO3_001/SrTiO3_001_2x2_mcmc_structures_100.pkl" \
--save_folder "SrTiO3_001/clustering" --nff_model_type "PaiNN" \
--nff_paths "tutorials/data/SrTiO3_001/nff/model01/best_model" \
"tutorials/data/SrTiO3_001/nff/model02/best_model" \
"tutorials/data/SrTiO3_001/nff/model03/best_model" \
--clustering_metric "force_std" --cutoff_criterion "distance" \
--clustering_cutoff 0.2 --nff_device "cuda"@article{duMachinelearningacceleratedSimulationsEnable2023,
title = {Machine-Learning-Accelerated Simulations to Enable Automatic Surface Reconstruction},
author = {Du, Xiaochen and Damewood, James K. and Lunger, Jaclyn R. and Millan, Reisel and Yildiz, Bilge and Li, Lin and {G{\'o}mez-Bombarelli}, Rafael},
year = {2023},
month = dec,
journal = {Nature Computational Science},
pages = {1--11},
publisher = {Nature Publishing Group},
issn = {2662-8457},
doi = {10.1038/s43588-023-00571-7},
urldate = {2023-12-07},
keywords = {Computational methods,Computational science,Software,Surface chemistry}
}VSSR-MC is under active development, if you encounter any bugs in installation and usage, please open an issue. We appreciate your contributions!