Computational Input Generation

SkillFiles & storage

Use 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.

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

TaskTool
VASP input (INCAR/POSCAR/KPOINTS)catgo_vasp_generate
Quantum ESPRESSO pw.x inputcatgo_qe_generate
LAMMPS input + data filecatgo_lammps_generate
Multi-stage LAMMPS simulationcatgo_lammps_sequential
ML potential relaxationcatgo_optimize
Single-point energy/forcescatgo_energy
List available calculatorscatgo_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 systems
  • chgnet: Good for oxides
  • m3gnet: General purpose
  • emt: 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

  1. catgo_optimize(calculator="mace", fmax=0.05) → 2. catgo_vasp_generate(calculation_type="opt")

VASP Slab Calculation

  1. 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