agat 8.0.0

Atomic Graph ATtention networks for predicting atomic energies and forces.

AGAT (Atomic Graph ATtention networks)

The PyTorch backend AGAT is on the way...

Using AGAT

The documentation of AGAT API is available.

Installation

Install with conda environment

• Create a new environment

  conda create -n agat python==3.10
  

• Activate the environment

  conda activate agat
  

• Install PyTorch,
  Navigate to the installation page and choose you platform. For example (GPU):

  conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
  

• Install dgl.
  Please navigate to the Get Started page of dgl. For example (GPU):

  conda install -c dglteam/label/cu118 dgl
  

• Install AGAT package

  pip install agat
  

• Install CUDA and CUDNN [Optional].

  • For HPC, you may load CUDA by checking module av, or you can contact your administrator for help.
  • CUDA Toolkit
  • cuDNN

Quick start

Prepare VASP calculations

Run VASP calculations at this step.

Collect paths of VASP calculations

• We provided examples of VASP outputs at VASP_calculations_example.
• Find all directories containing OUTCAR file:

  find . -name OUTCAR > paths.log
  

• Remove the string 'OUTCAR' in paths.log.

  sed -i 's/OUTCAR$//g' paths.log
  

• Specify the absolute paths in paths.log.

  sed -i "s#^.#${PWD}#g" paths.log
  

Build database

from agat.data import BuildDatabase
if __name__ == '__main__':
    database = BuildDatabase(mode_of_NN='ase_dist', num_of_cores=16)
    database.build()

Train AGAT model

from agat.model import Fit
f = Fit()
f.fit()

Application (geometry optimization)

from ase.optimize import BFGS
from agat.app import AgatCalculator

model_save_dir = 'agat_model'
graph_build_scheme_dir = 'dataset'

atoms = read('POSCAR')
calculator=AgatCalculator(model_save_dir,
                          graph_build_scheme_dir)
atoms = Atoms(atoms, calculator=calculator)
dyn = BFGS(atoms, trajectory='test.traj')
dyn.run(fmax=0.05)

Application (high-throughput prediction)

from agat.app.cata import HpAds

model_save_dir = 'agat_model'
graph_build_scheme_dir = 'dataset'
formula='NiCoFePdPt'

ha = HpAds(model_save_dir=model_save_dir, graph_build_scheme_dir=graph_build_scheme_dir)
ha.run(formula=formula)

For more custom manipulations, see our documentation page.

Some default parameters

agat/default_parameters.py; Explanations: docs/sphinx/source/Default parameters.md.

Change log

Please check Change_log.md