GPAW (Python DFT)

SkillDev tools

Generate and manage GPAW Python-based DFT calculations. Use when the user requests GPAW, Python DFT, real-space grid DFT, or LCAO-DFT with ASE integration.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the GPAW (Python DFT) skill

What this skill tells your AI

The instructions your AI receives, as published by hello-qm/catgo-lrg in .claude/skills/gpaw/SKILL.md and read by ahel’s review.

When to Use

  • User explicitly requests GPAW
  • User wants tight ASE integration (optimize with ASE, calculate with GPAW)
  • User needs real-space grid, LCAO, or plane-wave modes in a single code
  • User wants Python-scripted DFT workflows (no input files, pure Python)

Prerequisites

  1. GPAW + ASE installed on HPC (gpaw --version, python -c "import gpaw")
  2. PAW datasets installed (gpaw install-data)
  3. Structure loaded in viewer — verify with catgo_view(action="get_state")

Workflow Steps

1. Verify structure

catgo_view(action="get_state")

2. Create workflow

catgo_workflow_engine(action="create", params={"name": "GPAW PBE relaxation"})

3. Add GPAW task via shell script

CatGo does not yet have a native GPAW engine. Use task_type: "shell" with a Python script.

catgo_workflow_engine(action="add_task", params={
  "workflow_id": "wf_xxx",
  "task_type": "shell",
  "name": "gpaw_relax",
  "command": "python gpaw_relax.py",
  "input_files": {
    "gpaw_relax.py": "<script content>",
    "structure.json": "<pymatgen dict>"
  },
  "system_name": "TiO2_relax"
})

When a @register_engine("gpaw") is added to CatGo, use task_type: "geo_opt" with software: "gpaw" instead.

Script Template — SCF

from ase.io import read
from gpaw import GPAW, PW

atoms = read('structure.json')

calc = GPAW(
    mode=PW(500),            # Plane-wave mode, 500 eV cutoff
    xc='PBE',
    kpts={'density': 3.0},   # ~0.03 A^-1 k-point density
    txt='gpaw_scf.txt',
    occupations={'name': 'fermi-dirac', 'width': 0.05},
    convergence={'energy': 1e-5},
)

atoms.calc = calc
energy = atoms.get_potential_energy()
print(f'Total energy: {energy:.6f} eV')

Script Template — Relaxation

from ase.io import read, write
from ase.optimize import BFGS
from ase.constraints import FixAtoms
from gpaw import GPAW, PW

atoms = read('structure.json')

# Freeze bottom layers for slabs
c = FixAtoms(indices=[i for i, a in enumerate(atoms)
                      if a.position[2] < atoms.cell[2][2] * 0.4])
atoms.set_constraint(c)

calc = GPAW(
    mode=PW(500),
    xc='PBE',
    kpts={'density': 3.0},
    txt='gpaw_relax.txt',
    convergence={'energy': 1e-5},
)
atoms.calc = calc

opt = BFGS(atoms, trajectory='relax.traj', logfile='relax.log')
opt.run(fmax=0.02)

write('CONTCAR.vasp', atoms)

Parameter Guidance

ParameterTypical valueNotes
modePW(500)Plane-wave cutoff in eV; PW(600) for accurate forces
modeLCAO(dzp)LCAO mode for large systems (1000+ atoms)
xc'PBE'Also: 'RPBE', 'BEEF-vdW', 'mBEEF'
kpts{'density': 3.0}Auto k-mesh; higher = denser
convergence{'energy': 1e-5}In eV; tighten for phonon calcs
occupationsfermi-dirac, 0.05Smearing width in eV
parallel{'domain': 2, 'band': 2}Domain decomposition for MPI

Calculation Modes

ModeBest forSpeed
PW (plane-wave)Accurate bulk/surfaceModerate
LCAOLarge systems, screeningFast
FD (finite-difference)Real-space, nanostructuresSlow but flexible

Common Pitfalls

  1. Forgetting txt parameter — without it, GPAW writes no log and debugging is impossible
  2. LCAO basis not installed — run gpaw install-data with --basis flag
  3. Memory for large PW calculations — GPAW PW mode stores wavefunctions in memory; use LCAO for >500 atoms
  4. No restart file — add calc.write('checkpoint.gpw') after SCF for restart capability
  5. Parallel decomposition mismatchdomain * band * kpt must equal total MPI ranks
  6. Slab k-points — use kpts={'size': (N, N, 1)} to avoid k-points along vacuum direction

Signals

GitHub stars
196
Forks
23
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
gpaw
Source
github.com/hello-qm/catgo-lrg