ken2403 / schnettriple

SchNetTriple - SchNet with three-body interaction.

SchNetTriple

SchNetTriple is an atomistic neural network potential modeled by adding the three-body interaction inspired by the angular symmetry fucntion of ANI-1[3] to learning process of SchNet [1, 2].

Requirements:

• python 3
• ASE
• numpy
• PyTorch (>=1.7)
• schnetpack
• h5py
• Optional: tensorboardX

Note: We recommend using a GPU for training the neural networks.

Installation

Install this project by git.

Clone the repository

git clone https://github.com/ken2403/schnettriple.git
cd schnetpack

Install requirements

pip install -r requirements.txt

Install SchNetTriple

pip install .

You're ready to go!

Getting started

The example scripts provided by SchNetTriple are inserted into your PATH during installation.

Preparing the dataset

Prepare the dataset in ase database format. Or prepare a atoms .xyz file and convert it to ase database using the script provided by schnetpack.

spk_parse.py  /path_to_xyz  /save_path_to_db

Preparing argument file

Prepare the .json file that defines the hyperparameters for training. The example file can be found in schnettriple/src/scripts/ If the argument .json file is ready, you can train the model like this way.

snt_run  from_json  /path_to_train.json

Learned model are saved as best_model in model direcctory.

Evaluating learned model

The same script can also be used to evaluate the learned model. Prepare a .json file for evaluation.

snt_run  from_json  /path_to_eval.json

Write a result file evaluation.txt into the model directory.

Using POSCAR file as an input

You can also input one POSCAR structure file into the learned model to calculate total energy and interatomic forces. This is useful for phonon calculations with phonopy.

snt_run  from_poscar  /path_to_POSCAR  /path_to_learned_model  --cutoff  cutoff_radious  [--cuda]

If GPU is availabel, set --cuda. The calculation results are written to schnettriple_run.xml file and saved in the same directory as the POSCAR.

References

• [1] K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, K.-R. Müller, SchNet - A deep learning architecture for molecules and materials. J. Chem. Phys. 148, 241722 (2018). link

• [2] K. T. Schütt, P. Kessel, M. Gastegger, K. A. Nicoli, A. Tkatchenko, K.-R. Müller, SchNetPack: A deep learning toolbox for atomistic systems. J. Chem. Theory Comput. 15, 448–455 (2019). link

• [3] J. S. Smith, O. Isayev, A. E. Roitberg, ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci. 8, 3192–3203 (2017). link

• [4] X. Gao, F. Ramezanghorbani, O. Isayev, J. S. Smith, A. E. Roitberg, TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials. J. Chem. Inf. Model. 60, 3408–3415 (2020). link