GPAW (Python DFT)
SkillDev toolsGenerate 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.
No other account needed.
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
- GPAW + ASE installed on HPC (
gpaw --version,python -c "import gpaw") - PAW datasets installed (
gpaw install-data) - 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
| Parameter | Typical value | Notes |
|---|---|---|
| mode | PW(500) | Plane-wave cutoff in eV; PW(600) for accurate forces |
| mode | LCAO(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 |
| occupations | fermi-dirac, 0.05 | Smearing width in eV |
| parallel | {'domain': 2, 'band': 2} | Domain decomposition for MPI |
Calculation Modes
| Mode | Best for | Speed |
|---|---|---|
| PW (plane-wave) | Accurate bulk/surface | Moderate |
| LCAO | Large systems, screening | Fast |
| FD (finite-difference) | Real-space, nanostructures | Slow but flexible |
Common Pitfalls
- Forgetting
txtparameter — without it, GPAW writes no log and debugging is impossible - LCAO basis not installed — run
gpaw install-datawith--basisflag - Memory for large PW calculations — GPAW PW mode stores wavefunctions in memory; use LCAO for >500 atoms
- No restart file — add
calc.write('checkpoint.gpw')after SCF for restart capability - Parallel decomposition mismatch —
domain * band * kptmust equal total MPI ranks - 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