Computational Input Generation
SkillFiles & storageUse when the user asks to generate DFT input files (VASP, Quantum ESPRESSO, LAMMPS), optimize structures with ML potentials (MACE, CHGNet, M3GNet), compute energy, or set up any computational chemistry calculation.
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 Computational Input Generation skill
What this skill tells your AI
The instructions your AI receives, as published by hello-qm/catgo-lrg in catbot-plugin/skills/computational-input/SKILL.md and read by ahel’s review.
Quick Decision Guide
| Task | Tool |
|---|---|
| VASP input (INCAR/POSCAR/KPOINTS) | catgo_vasp_generate |
| Quantum ESPRESSO pw.x input | catgo_qe_generate |
| LAMMPS input + data file | catgo_lammps_generate |
| Multi-stage LAMMPS simulation | catgo_lammps_sequential |
| ML potential relaxation | catgo_optimize |
| Single-point energy/forces | catgo_energy |
| List available calculators | catgo_calculators |
VASP Input
catgo_vasp_generate
- Calculation types:
opt,scf,freq,bader,dos,ddec,elf - Key params:
encut(default 450 eV),gga("PE"=PBE),ispin(2=spin-polarized),ivdw(12=D3-BJ),kspacing,fixed_indices/fixed_z_below
Common patterns:
- Bulk optimization:
calculation_type="opt",isif=3(relax cell+ions) - Slab optimization:
calculation_type="opt",isif=2,fixed_z_below=Z - DOS:
calculation_type="dos", dense k-mesh
Call catgo_vasp_calc_types to list all available types with defaults.
Quantum ESPRESSO Input
catgo_qe_generate
- Calculation types:
scf,relax,vc-relax,nscf,bands - Key params:
ecutwfc(default 60 Ry),ecutrho(default 480 Ry),kspacing,occupations,smearing("mv"=Marzari-Vanderbilt),nspin
Call catgo_qe_templates for recommended settings per calculation type.
LAMMPS Input
catgo_lammps_generate
- Simulation types:
minimize,nve,nvt,npt - Key params:
pair_style,pair_coeff,potential_file,temperature,pressure
catgo_lammps_sequential — Multi-stage MD protocol:
{"stages": [
{"name": "minimize", "simulation_type": "minimize"},
{"name": "heat", "simulation_type": "nvt", "temperature": 300, "run_steps": 10000},
{"name": "equilibrate", "simulation_type": "npt", "temperature": 300, "run_steps": 50000},
{"name": "production", "simulation_type": "nvt", "temperature": 300, "run_steps": 100000}
]}
Call catgo_lammps_pair_styles for available force fields.
Call catgo_lammps_validate before generating to check configuration.
ML Potential Optimization
catgo_optimize
Quick relaxation using ML interatomic potentials:
mace: Best accuracy for most systemschgnet: Good for oxidesm3gnet: General purposeemt: Fast, metals only (testing)
Params: fmax (default 0.05 eV/A), max_steps (200), relax_cell (True to relax lattice)
catgo_energy — Single-point energy + forces without optimization.
Workflow Recipes
ML Pre-Optimization then DFT
catgo_optimize(calculator="mace", fmax=0.05)→ 2.catgo_vasp_generate(calculation_type="opt")
VASP Slab Calculation
- Build slab → 2.
catgo_vasp_generate(calculation_type="opt", isif=2, fixed_z_below=Z, encut=520)
Signals
- GitHub stars
- 196
- Forks
- 23
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
computational-input- Source
- github.com/hello-qm/catgo-lrg