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 calculatorsforce-fields — pre-optimize with MM before QM; GAFF2 validationdocking — 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
1---2name: qm-dft3description: 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).4---56# QM/DFT — Quantum Chemistry Calculations78Quantum 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).910## When to Use This Skill1112- Geometry optimization with QM accuracy (beyond MM force fields)13- Reaction energetics: transition states, barrier heights, IRC14- Spectroscopy: IR/Raman frequencies, NMR shifts, UV-Vis (TD-DFT)15- Partial charge calculation: RESP, ESP, NBO, Mulliken16- pKa estimation, protonation states17- Conformer search and ranking (CREST + xTB)18- Parametrization validation: compare QM vs force field energies19- Property prediction: dipole moment, polarizability, HOMO/LUMO gaps2021## Method Cost Hierarchy2223| Method | Cost | Accuracy | Use case |24|--------|------|----------|----------|25| GFN-FF | O(N²) | ~MM | Pre-screening, conformers |26| GFN2-xTB | O(N²·8) | Good | Conformers, pre-opt, pKa |27| r²SCAN-3c | O(N³) | Very good | Routine geometry opt |28| B3LYP-D3BJ/def2-SVP | O(N⁴) | Good | Drug-like molecules opt |29| B3LYP-D3BJ/def2-TZVP | O(N⁴) | Better | Single-point on opt geom |30| ωB97X-D/def2-TZVP | O(N⁴) | Very good | Reaction barriers, CT states |31| DLPNO-CCSD(T)/CBS | O(N⁵⁺) | Benchmark | High-accuracy energetics |3233## Quick Start3435```python36# xTB geometry optimization (fastest QM-level method)37import subprocess3839result = subprocess.run(40 ['xtb', 'mol.xyz', '--opt', '--gfn', '2', '--alpb', 'water'],41 capture_output=True, text=True, cwd='workdir/'42)43# Output: xtbopt.xyz (optimized), xtbopt.log4445# Parse final energy46for line in result.stdout.split('\n'):47 if 'TOTAL ENERGY' in line:48 energy = float(line.split()[3]) # Hartree49 print(f"E = {energy:.8f} Eh")50```5152```python53# ORCA single-point DFT (via subprocess)54orca_input = """\55! B3LYP D3BJ def2-SVP TightSCF56%pal nprocs 4 end57%maxcore 20005859* xyzfile 0 1 mol.xyz60"""6162with open('sp.inp', 'w') as f:63 f.write(orca_input)6465result = subprocess.run(['orca', 'sp.inp'], capture_output=True, text=True)66# Parse with chem_qm.py: python chem_qm.py --parse sp.out67```6869## Router — What to Read7071| Task | Reference |72|------|-----------|73| DFT functionals, basis sets, dispersion, Jacob's ladder | `references/dft-theory.md` |74| ORCA: input syntax, optimization, freq, NMR, TD-DFT, output parsing | `references/orca-practical.md` |75| xTB/GFN2: CLI, Python (tblite), CREST, solvation, pKa | `references/xtb-semiempirical.md` |76| PySCF: Python QM, HF/DFT/MP2/CCSD, NMR, ESP charges | `references/pyscf-python.md` |77| Standard recipes: opt→freq, RESP, UV-Vis, barriers, NBO | `references/common-workflows.md` |7879## Key Tools8081| Tool | Version | Install | Role |82|------|---------|---------|------|83| ORCA | 6.0 | orca-forum.org (free) | General QM: DFT, MP2, CCSD(T), TD-DFT |84| xTB | 6.7 | `conda install -c conda-forge xtb` | Fast semi-empirical |85| tblite | 0.3 | `pip install tblite` | xTB Python API |86| CREST | 3.0 | `conda install -c conda-forge crest` | Conformer/ensemble search |87| PySCF | 2.7 | `pip install pyscf` | Python-native QM |88| Psi4 | 1.9 | `conda install -c conda-forge psi4` | Python QM + MP2/CCSD |8990## Related Skills9192- `ase` — structure building, ASE-driven optimization with ORCA/xTB calculators93- `force-fields` — pre-optimize with MM before QM; GAFF2 validation94- `docking` — QM refinement of top docking poses95- scripts: `chem_qm.py` — ORCA/Gaussian input gen + output parsing (ALKYL native)96- `scientific-skills:rowan` — cloud QM (DFT, pKa, Chai-1) without local ORCA install
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