pfnet-research/charge_transfer_nnp

Graph neural network potential with charge transfer

Charge transfer modeling with neural network potential

Installation

1. Install PyTorch. This package is tested on
   • CUDA==11.1
   • Python==3.8.11
   • PyTorch==1.8.2
2. Install PyTorch Geometric. Note that this package does not work with the latest PyG's major version. Please install torch_geometric<=1.7.2. For example,

export CUDA=cu111
export TORCH=1.8.0
python -m pip install torch-scatter==2.0.8 torch-sparse==0.6.11 torch-geometric==1.7.2 -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html

3. Install other dependencies

python -m pip install -r requirements.txt

4. Install this package

python -m pip install .

Usage

Prepare 4G-HDNNP datasets [1, 2]

Datasets are taken from 4G-HDNNPs [1, 2].

cd datasets
# If you fail to download the below url, try to download `datasets.tar.gz` directly
# from https://archive.materialscloud.org/record/2020.137
wget "https://archive.materialscloud.org/record/file?filename=datasets.tar.gz&record_id=629"
mv file\?filename\=datasets.tar.gz\&record_id\=629 datasets_runner.tar.gz
tar xzvf datasets_runner.tar.gz
python parse_datasets_runner.py 

Now you have the following file structure:

+
|- datasets
|   |- parse_datasets_runner.py
|   |- datasets_runner.tar.gz
|   |- datasets_runner
|   |- Ag_cluster
|   |   |- 0.json
|   |   |- ...
|   |- AuMgO
|   |- Carbon_chain
|   |- NaCl
-...

The units of the processed datasets are angstrom for distance, eV for total energies, eV/angstrom for forces, and elemental charge for charges.

Train network

All settings for training are described with a YAML file. estorch-train command start to train a network.

estorch-train configs/minimal.yaml

Note that estorch-train is assumed to be executed at the top of this reposity, because a directory path for a dataset, dataset_file_name in the YAML file, may be relative. The result are stored under root directory specified in the YAML file.

Example configurations are provided in NequIP [3-4], which this package is developed on the top of. There are a few additional options for this package

# in YAML file
use_charge: true  # iff true, use total_charge and predict atomic charges
use_ele: false  # iff true, calculate electrostatic term
use_qeq: true  # iff true, 
pbc: false  # iff true, is periodic system

Training can be automatically started and restarted by using estorch-requeue command

estorch-requeue configs/minimal_requeue.yaml

We provide some configurations for reproducing experiments.

• configs/baseline/{system}_baseline.yaml: Baseline model (NequIP) trained with 4G-HDNNP dataset
• configs/charge/{system}_charge.yaml: Predict directly atomic charges and add electrostatic energy
• configs/qeq/{system}_qeq.yaml: Predict atomic charges via charge equilibration scheme (Qeq) and add electrostatic energy

	YAML files	use_charge	use_ele	use_qeq
Base (NequIP)	configs/baseline/*.yaml	false	false	false
Base w/ E_ele	configs/charge/*.yaml	true	true	false
Base w/ Qeq	configs/qeq/*.yaml	true	true	true

How to load custom dataset

A loaded dataset is controlled by dataset and dataset_file_name keywords in the YAML file.

# Example: in configs/minimal.yaml
dataset: estorch.datasets.fghdnnp.FGHDNNPDataset
dataset_file_name: datasets/Carbon_chain

dataset keyword specifies a module for creating datasets, which inherit torch_geometric.data.Dataset.

dataset_file_name keyword specifies a directory path for a raw dataset. When we set dataset: estorch.datasets.fghdnnp.FGHDNNPDataset, this directory contains JSON files for structures. Each JSON file has the following keys:

{
    "pos": ...,  // (num_atoms, 3) float array, positions of atoms in cartesian coordinates
    "symbols": ...,  // (num_atoms, ) str array, atomic species
    "charges": ...,  // (num_atoms, ) float array, atomic charges
    "total_energy": ...,  // float
    "forces": ...,  // (num_atoms, 3) float array, forces acting on atoms
    "total_charge": ...  // float
}

For more details, please read NequIP's developer tutorial.

References

1. Tsz Wai Ko, Jonas A. Finkler, Stefan Goedecker, Jörg Behler, A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer, Nat. Commun. 12, 398 (2021).
2. https://archive.materialscloud.org/record/2020.137
3. S. Batzner et al., arxiv:2101.03164
4. https://github.com/mir-group/nequip