Skip to main content

Atomic Graph ATtention networks for predicting atomic energies and forces.

Project description

AGAT (Atomic Graph ATtention networks)

DOI GitHub PyPI - Downloads PyPI - Wheel GitHub tag (with filter)

Installation

Install with conda environment

  • Create a new environment
conda create -n agat python==3.10
  • Activate the environment
conda activate agat
  • Install package
pip install agat
conda install -c dglteam/label/cu118 dgl

Using AGAT

The documentation of AGAT API is available.

Quick start

Prepare VASP calculations

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 AgatDatabase
if __name__ == '__main__':
    ad = AgatDatabase(mode_of_NN='ase_dist', num_of_cores=2)
    ad.build()

Train AGAT model

from agat.model import Train
at = Train()
at.fit_energy_model()
at.fit_force_model()

Model prediction

from agat.app import GatApp
energy_model_save_dir = os.path.join('out_file', 'energy_ckpt')
force_model_save_dir = os.path.join('out_file', 'force_ckpt')
graph_build_scheme_dir = 'dataset'
app = GatApp(energy_model_save_dir, force_model_save_dir, graph_build_scheme_dir)
graph, info = app.get_graph('POSCAR')
energy = app.get_energy(graph)
forces = app.get_forces(graph)

Geometry optimization

from ase.io import read
from ase.optimize import BFGS
from agat.app import GatAseCalculator
from agat.default_parameters import default_hp_config
poscar = read('POSCAR')
calculator=GatAseCalculator(energy_model_save_dir,
                            force_model_save_dir,
                            graph_build_scheme_dir)
poscar = Atoms(poscar, calculator=calculator)
dyn = BFGS(poscar, trajectory='test.traj')
dyn.run(**default_hp_config['opt_config'])

Change log

Please check Change_log.md

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

agat-7.12.tar.gz (58.3 kB view details)

Uploaded Source

Built Distribution

agat-7.12-py3-none-any.whl (62.4 kB view details)

Uploaded Python 3

File details

Details for the file agat-7.12.tar.gz.

File metadata

  • Download URL: agat-7.12.tar.gz
  • Upload date:
  • Size: 58.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.5

File hashes

Hashes for agat-7.12.tar.gz
Algorithm Hash digest
SHA256 569eddf1cfe2782686f5099a817b2ef5da1fc8efa1dfddce6ed848b88b28d1cd
MD5 5d2cf63530b7000bc3b2fb802bcbade2
BLAKE2b-256 19d2149d41de85fc365b7b1b391e660728510fe0166540a590f54821e24f1818

See more details on using hashes here.

File details

Details for the file agat-7.12-py3-none-any.whl.

File metadata

  • Download URL: agat-7.12-py3-none-any.whl
  • Upload date:
  • Size: 62.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.5

File hashes

Hashes for agat-7.12-py3-none-any.whl
Algorithm Hash digest
SHA256 8e62c5682faaa6d4ccc3836f4365c784cba20c0b86fa430ae9dc6f0cd6e7e6a9
MD5 bc52421b74eb6b6ef82c7f8fe358d58e
BLAKE2b-256 c615fbb5a92afc47ac34236470fb7160b825a183d1df39e6d9d9076d3181c4f0

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page