Qm Dft

QM/DFT — Quantum Chemistry Calculations

Quantum mechanics-based methods compute electronic structure explicitly — enabling bond breaking/forming, spectroscopic properties, and accurate energetics beyond force fields. Python ecosystem: ORCA (best free QM, subprocess), xTB/tblite (fast semi-empirical, Python API), PySCF (pure Python, scriptable).

When to Use This Skill

• Geometry optimization with QM accuracy (beyond MM force fields)
• Reaction energetics: transition states, barrier heights, IRC
• Spectroscopy: IR/Raman frequencies, NMR shifts, UV-Vis (TD-DFT)
• Partial charge calculation: RESP, ESP, NBO, Mulliken
• pKa estimation, protonation states
• Conformer search and ranking (CREST + xTB)
• Parametrization validation: compare QM vs force field energies
• Property prediction: dipole moment, polarizability, HOMO/LUMO gaps

Method Cost Hierarchy

| Method | Cost | Accuracy | Use case | |--------|------|----------|----------| | GFN-FF | O(N²) | ~MM | Pre-screening, conformers | | GFN2-xTB | O(N²·8) | Good | Conformers, pre-opt, pKa | | r²SCAN-3c | O(N³) | Very good | Routine geometry opt | | B3LYP-D3BJ/def2-SVP | O(N⁴) | Good | Drug-like molecules opt | | B3LYP-D3BJ/def2-TZVP | O(N⁴) | Better | Single-point on opt geom | | ωB97X-D/def2-TZVP | O(N⁴) | Very good | Reaction barriers, CT states | | DLPNO-CCSD(T)/CBS | O(N⁵⁺) | Benchmark | High-accuracy energetics |

Quick Start

# xTB geometry optimization (fastest QM-level method)
import subprocess

result = subprocess.run(
    ['xtb', 'mol.xyz', '--opt', '--gfn', '2', '--alpb', 'water'],
    capture_output=True, text=True, cwd='workdir/'
)
# Output: xtbopt.xyz (optimized), xtbopt.log

# Parse final energy
for line in result.stdout.split('\n'):
    if 'TOTAL ENERGY' in line:
        energy = float(line.split()[3])  # Hartree
        print(f"E = {energy:.8f} Eh")

# ORCA single-point DFT (via subprocess)
orca_input = """\
! B3LYP D3BJ def2-SVP TightSCF
%pal nprocs 4 end
%maxcore 2000

* xyzfile 0 1 mol.xyz
"""

with open('sp.inp', 'w') as f:
    f.write(orca_input)

result = subprocess.run(['orca', 'sp.inp'], capture_output=True, text=True)
# Parse with chem_qm.py: python chem_qm.py --parse sp.out

Router — What to Read

| Task | Reference | |------|-----------| | DFT functionals, basis sets, dispersion, Jacob's ladder | references/dft-theory.md | | ORCA: input syntax, optimization, freq, NMR, TD-DFT, output parsing | references/orca-practical.md | | xTB/GFN2: CLI, Python (tblite), CREST, solvation, pKa | references/xtb-semiempirical.md | | PySCF: Python QM, HF/DFT/MP2/CCSD, NMR, ESP charges | references/pyscf-python.md | | Standard recipes: opt→freq, RESP, UV-Vis, barriers, NBO | references/common-workflows.md |

Key Tools

| Tool | Version | Install | Role | |------|---------|---------|------| | ORCA | 6.0 | orca-forum.org (free) | General QM: DFT, MP2, CCSD(T), TD-DFT | | xTB | 6.7 | conda install -c conda-forge xtb | Fast semi-empirical | | tblite | 0.3 | pip install tblite | xTB Python API | | CREST | 3.0 | conda install -c conda-forge crest | Conformer/ensemble search | | PySCF | 2.7 | pip install pyscf | Python-native QM | | Psi4 | 1.9 | conda install -c conda-forge psi4 | Python QM + MP2/CCSD |

Related Skills

• ase — structure building, ASE-driven optimization with ORCA/xTB calculators
• force-fields — pre-optimize with MM before QM; GAFF2 validation
• docking — QM refinement of top docking poses
• scripts: chem_qm.py — ORCA/Gaussian input gen + output parsing (ALKYL native)
• scientific-skills:rowan — cloud QM (DFT, pKa, Chai-1) without local ORCA install

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Kdevos12
• Source: Kdevos12/ALKYL
• License: MIT

Install and usage instructions live in the source repository linked above.