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ALIGNN

Property Predictors

atomgptlab/alignn

Property Predictors¶

Single-property ALIGNN checkpoints are invoked through pretrained.py.

CLI help¶

pretrained.py -h

Example — formation energy (JARVIS-DFT)¶

pretrained.py \
  --model_name jv_formation_energy_peratom_alignn \
  --file_format poscar \
  --file_path alignn/examples/sample_data/POSCAR-JVASP-10.vasp

Available models¶

A non-exhaustive list — names match the --model_name flag:

Target	Model name
Formation energy per atom (JARVIS-DFT)	`jv_formation_energy_peratom_alignn`
Bandgap — OPT88vdW	`jv_optb88vdw_bandgap_alignn`
Bandgap — MBJ	`jv_mbj_bandgap_alignn`
Total energy per atom (JARVIS-DFT)	`jv_optb88vdw_total_energy_alignn`
Ehull	`jv_ehull_alignn`
Bulk modulus (K_v)	`jv_bulk_modulus_kv_alignn`
Shear modulus (G_v)	`jv_shear_modulus_gv_alignn`
Dielectric (εx, OPT)	`jv_epsx_alignn`
Max. piezo dielectric (DFPT)	`jv_dfpt_piezo_max_dielectric_alignn`
Spillage	`jv_spillage_alignn`
SLME	`jv_slme_alignn`
Magnetic moment	`jv_magmom_oszicar_alignn`
Raman	`jv_raman_alignn`
Superconductor T_c	`jv_supercon_tc_alignn`
Interface CBM / VBM	`intermat_cbm`, `intermat_vbm`
hMOF CO₂ adsorption	`hmof_co2_absp_alignn`

See all_models_alignn.json for the full machine-readable list.

File formats¶

Pass --file_format matching your structure file:

poscar — VASP POSCAR
cif
xyz
pdb

Using from Python¶

from alignn.pretrained import get_prediction

prediction = get_prediction(
    model_name="jv_formation_energy_peratom_alignn",
    atoms=my_jarvis_atoms,  # jarvis.core.atoms.Atoms
)
print(prediction)

See also¶