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Pre-trained ALIGNN-FF

ALIGNN-FF is a universal graph neural network force-field. Several pretrained versions are shipped with the package under alignn/ff/ and on Figshare.

CLI

run_alignn_ff.py -h

Common tasks:

# Single-point energy on the unrelaxed structure
run_alignn_ff.py --file_path POSCAR --task="unrelaxed_energy"

# Structure optimization
run_alignn_ff.py --file_path POSCAR --task="optimize"

# Energy vs. volume curve
run_alignn_ff.py --file_path POSCAR --task="ev_curve"

Additional tasks supported by the CLI include phonon calculations, interface gamma-surface scans, and simple MD — run -h to see the full set.

Available checkpoints

Model folders bundled with the package:

  • v10.30.2024_dft_3d_307k — trained on 307k JARVIS-DFT configurations (Oct 2024)
  • v12.2.2024_dft_3d_307k — updated December 2024 release
  • v2024.12.12_dft_3d_multi_prop — multi-property (energy + atomwise targets)
  • alignnff_wt01 — weighted loss variant
  • alex_band_gap, jv_mbj_bandgap_alignn — bandgap-oriented models

Additional models are listed in all_models_alignn_atomwise.json.

Default path helper

from alignn.ff.ff import default_path
print(default_path())  # directory containing the default checkpoint

Using it programmatically

See the ASE calculator page for a full worked example covering relaxation and an EV curve.

See also