Qm dft

Claude Plugin for CompChem , Drug Discovery & Organic Chemistry reasoning

Install

npx -y skills add Kdevos12/ALKYL --skill qm-dft

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Use when working with quantum chemistry (QM) and DFT calculations. Covers DFT functional/basis set selection, ORCA input/output, xTB semi-empirical methods (GFN2, CREST), PySCF Python-native QM, and standard workflows (geometry opt, frequencies, NMR, TD-DFT, reaction barriers, RESP charges).

SKILL.md

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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