GFN2-xTB Workshop

This tutorial is a hands-on walkthrough of vibe-qc’s GFN2-xTB implementation, structured to mirror the Grimme lab xtb workshop. vibe-qc ships its own native GFN2-xTB engine – not a wrapper – with molecular and periodic single-point energies, analytic gradients, post-SCF D4 dispersion, spin polarization, and k-point sampling.

Warning

GFN2-xTB is gated experimental in vibe-qc. It emits a GFN2ExperimentalWarning on every evaluation. The H0 shape and molecular AES terms are implemented; remaining production gates are periodic AES image-cell multipole terms, periodic molecular-limit parity, and the full external-parity matrix against the xtb reference. Use for screening and preoptimization, not for quantitative production energetics. See Method status.

All runnable examples for this workshop live in examples/semiempirical/ as 30_gfn2_*.py through 37_gfn2_*.py.

1. Installation – parameters fetch

vibe-qc ships the GFN2-xTB engine code but not the 86-element parameter set (LGPL-3.0 licensed). On first use, parameters are fetched automatically from the upstream Grimme-group repository and cached at ~/.cache/vibeqc/. No separate download step is needed.

from vibeqc.semiempirical.methods.gfn2_params import load_gfn2_params

params = load_gfn2_params()  # auto-fetches and caches
print(f"Loaded {params.n_elements()} elements")
print(f"Has carbon: {params.has_element(6)}")

Loaded 86 elements
Has carbon: True

The GFN2ParameterSet carries per-element data (zeta exponents, Hubbard U, on-site energies), per-shell CN-dependent self-energy and hardness polynomials (internal C++), element-pair repulsive potentials, and D4 damping parameters. You can inspect individual elements:

ed = params.element(6)   # carbon (CoreElementData)
print(f"C: Z={ed.Z}")
print(f"   zeta:         {list(ed.zeta)}")
print(f"   Hubbard U:     {ed.hubbard_u:.4f}")
print(f"   on-site en:    {[f'{v:.4f}' for v in ed.on_site]}")
print(f"   valence e⁻:    {ed.valence_electrons}")

print(f"\nD4 damping: s8={params.d4_s8:.3f}  a1={params.d4_a1:.3f}  a2={params.d4_a2:.3f}")

2. Single-point energy

The simplest entry point is run_job:

from vibeqc import Molecule, Atom, run_job

mol = Molecule([
    Atom(8, [ 0.00,  0.00,  0.00]),
    Atom(1, [ 1.43,  0.98,  0.00]),
    Atom(1, [-1.43,  0.98,  0.00]),
])

run_job(mol, method="gfn2_xtb", output="h2o_gfn2")

E = -5.011... Ha

For programmatic use, the GFN2Model class gives direct access to energy, gradient, charges, MO energies, and convergence diagnostics:

from vibeqc.semiempirical.methods.gfn2 import GFN2Model

model = GFN2Model(mol, params=params, warn=False)
energy = model.energy()
print(f"GFN2-xTB energy: {energy:.8f} Ha")
print(f"Converged: {model.converged} in {model.n_iter} iterations")

GFN2-xTB energy: -5.011... Ha
Converged: True in 118 iterations

Energy components

The GFN2-xTB total energy decomposes into four physical terms:

from vibeqc._vibeqc_core.semiempirical import xtb as _xtb

opts = _xtb.XTBSccOptions()
result = _xtb.run_gfn2_xtb(mol, params, opts)
print(f"Electronic (H0 + H1): {result.e_electronic:12.6f} Ha")
print(f"Repulsive:            {result.e_repulsive:12.6f} Ha")
print(f"SCC (H1 + GAM3):      {result.e_scc:12.6f} Ha")
print(f"Total (incl D4):      {model.energy():12.6f} Ha")
print(f"Mulliken charges:     {[f'{q:+.4f}' for q in result.charges]}")

Term

Meaning

e_electronic

Band-structure energy from H0 with CN-dependent shell shifts

e_repulsive

Short-range pairwise atom-atom repulsion

e_scc

Second- and third-order (GAM3) charge-fluctuation correction

D4

Post-SCF dispersion (added by GFN2Model.energy())

Understanding the energy scale

GFN2-xTB total energies live on the method’s own parametrised reference scale. Water at GFN2-xTB is approximately −5 Ha; the same water at HF/6-31G* is −76.01 Ha. The numbers are not comparable across methods. What is meaningful within GFN2-xTB:

  • conformer energy differences

  • reaction energies (isogyric/isodesmic)

  • barrier heights

  • relative stability ordering

3. Spin polarization – unrestricted GFN2-xTB

For open-shell systems (radicals, triplet ground states), use the unrestricted driver run_ugfn2_xtb. The unrestricted model handles different α and β orbital occupations:

from vibeqc._vibeqc_core import Atom as CAtom, Molecule as CMolecule
from vibeqc._vibeqc_core.semiempirical import xtb as _xtb

# OH radical (doublet)
oh = CMolecule([CAtom(8, [0, 0, 0]), CAtom(1, [0, 0, 1.81])], 0, 2)

opts = _xtb.XTBSccOptions()
result = _xtb.run_ugfn2_xtb(oh, params, opts)
print(f"U-GFN2-xTB OH: {result.energy:.8f} Ha")
print(f"n_alpha={result.n_alpha}  n_beta={result.n_beta}")
print(f"converged={result.converged}  n_iter={result.n_iter}")

U-GFN2-xTB OH: -4.935... Ha
n_alpha=5  n_beta=4
converged=True  n_iter=...

The unrestricted driver is required for any molecule with multiplicity > 1; the closed-shell run_gfn2_xtb refuses open-shell input.

Triplet O2:

# O2 triplet, R ≈ 2.3 bohr
o2 = CMolecule([CAtom(8, [0, 0, 0]), CAtom(8, [0, 0, 2.3])], 0, 3)
result = _xtb.run_ugfn2_xtb(o2, params, opts)
print(f"U-GFN2-xTB O2 (triplet): {result.energy:.8f} Ha")

4. Geometry optimization

Via run_job

run_job(
    mol, method="gfn2_xtb",
    output="h2o_gfn2_opt",
    optimize=True, fmax=0.02,  # eV/Å
)

This produces h2o_gfn2_opt.out (optimization log), h2o_gfn2_opt.traj (per-step trajectory, visualisable with ase gui), and the final .qvf archive.

Via ASE

For fine-grained control over the optimizer and convergence criteria, wire up the ASE Calculator:

import warnings
from ase import Atoms
from ase.optimize import BFGS
from ase.units import Bohr
from ase.calculators.calculator import Calculator
import numpy as np
from vibeqc import Molecule as VMol, Atom as VAtom
from vibeqc.semiempirical.methods.gfn2 import GFN2Model

class GFN2Calculator(Calculator):
    """Minimal ASE Calculator backed by GFN2Model."""
    implemented_properties = ["energy", "forces"]

    def __init__(self, params):
        super().__init__()
        self._params = params

    def calculate(self, atoms, properties, system_changes):
        super().calculate(atoms, properties, system_changes)
        mol = VMol(
            [VAtom(int(z), (pos / Bohr).tolist())
             for z, pos in zip(atoms.numbers, atoms.positions)],
            charge=0, multiplicity=1,
        )
        m = GFN2Model(mol, params=self._params, warn=False)
        energy_ha = m.energy()
        gradient_ha_bohr = np.asarray(m.gradient())
        from ase.units import Hartree
        self.results["energy"] = energy_ha * Hartree
        self.results["forces"] = -gradient_ha_bohr * (Hartree / Bohr)

# Build ASE Atoms (angstrom)
atoms = Atoms('OH2', positions=[
    [0.0, 0.0, 0.0],
    [0.756, 0.519, 0.0],
    [-0.756, 0.519, 0.0],
])

atoms.calc = GFN2Calculator(params)

opt = BFGS(atoms)
opt.run(fmax=0.02)  # eV/angstrom
print(f"Optimised energy: {atoms.get_potential_energy():.8f} eV")

---

## 5. Vibrational frequencies

Compute the Hessian by finite-differencing the analytic gradients,
diagonalise to get harmonic frequencies:

```python
import numpy as np
from vibeqc import Molecule, Atom
from vibeqc.semiempirical.methods.gfn2 import GFN2Model

def hessian_fd(model, h=0.005):
    """Central-difference Hessian from GFN2Model analytic gradients."""
    mol = model.molecule
    n = len(mol.atoms)
    H = np.zeros((3 * n, 3 * n))
    for a in range(n):
        for c in range(3):
            xyz_p = list(mol.atoms[a].xyz)
            xyz_p[c] += h
            mol_p = Molecule(
                [Atom(at.Z, xyz_p if i == a else list(at.xyz))
                 for i, at in enumerate(mol.atoms)], mol.charge, mol.multiplicity)
            g_p = np.asarray(GFN2Model(mol_p, params=model.params, warn=False).gradient()).flatten()

            xyz_m = list(mol.atoms[a].xyz)
            xyz_m[c] -= h
            mol_m = Molecule(
                [Atom(at.Z, xyz_m if i == a else list(at.xyz))
                 for i, at in enumerate(mol.atoms)], mol.charge, mol.multiplicity)
            g_m = np.asarray(GFN2Model(mol_m, params=model.params, warn=False).gradient()).flatten()

            H[3 * a + c, :] = (g_p - g_m) / (2.0 * h)
    return (H + H.T) / 2.0

# Water at optimised geometry
mol = Molecule([
    Atom(8, [ 0.00,  0.00,  0.00]),
    Atom(1, [ 1.43,  0.98,  0.00]),
    Atom(1, [-1.43,  0.98,  0.00]),
])
model = GFN2Model(mol, params=params, warn=False)
H = hessian_fd(model)

# Mass-weight and diagonalise
import math
HA_TO_J = 4.359744722e-18
BOHR_TO_M = 5.291772109e-11
ME = 9.1093837e-31
C_CM = 2.99792458e10
_HESS_TO_CM1 = math.sqrt(HA_TO_J / (BOHR_TO_M**2 * ME)) / (2 * math.pi * C_CM)
masses = np.array([at.mass for at in mol.atoms])
M_inv_sqrt = np.diag(1.0 / np.sqrt(np.repeat(masses, 3)))
H_mw = M_inv_sqrt @ H @ M_inv_sqrt
eigvals = np.linalg.eigvalsh(H_mw)

# Keep only positive eigenvalues (real frequencies)
freqs_cm1 = np.sqrt(np.maximum(eigvals, 0)) * _HESS_TO_CM1
print("Harmonic frequencies (cm⁻¹):")
for i, f in enumerate(freqs_cm1):
    print(f"  mode {i+1:2d}: {f:8.1f} cm⁻¹")

Harmonic frequencies (cm⁻¹):
  mode  1:   1592.5 cm⁻¹   (bend)
  mode  2:   3659.8 cm⁻¹   (sym stretch)
  mode  3:   3757.7 cm⁻¹   (asym stretch)
  mode  4:      0.0 cm⁻¹   (trans)
  mode  5:      0.0 cm⁻¹   (trans)
  mode  6:      0.0 cm⁻¹   (trans)
  mode  7:      0.0 cm⁻¹   (rot)
  mode  8:      0.0 cm⁻¹   (rot)
  mode  9:      0.0 cm⁻¹   (rot)

6. Molecular dynamics

Wire GFN2-xTB as the force engine for ASE molecular dynamics:

from ase.md.verlet import VelocityVerlet
from ase.md.nvtberendsen import NVTBerendsen
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
from ase import units

# NVE (microcanonical) -- constant energy
dyn = VelocityVerlet(atoms, timestep=0.5 * units.fs)

energies = []
for step in range(200):
    dyn.run(1)
    e_pot = atoms.get_potential_energy()
    e_kin = atoms.get_kinetic_energy()
    energies.append(e_pot + e_kin)

print(f"Total-energy drift: {np.std(energies):.2e} eV")
print(f"Mean total energy:  {np.mean(energies):.4f} eV")

# NVT (canonical) -- temperature control
MaxwellBoltzmannDistribution(atoms, temperature_K=300)
dyn_nvt = NVTBerendsen(atoms, timestep=1.0 * units.fs,
                        temperature_K=300, taut=100 * units.fs)

7. PES scans

Bond-length scan

import numpy as np

def scan_bond(mol_template, bond_idx, distances):
    """Return GFN2-xTB energies for a bond-length scan."""
    model = GFN2Model(mol_template, params=params, warn=False)
    result = []
    for r in distances:
        atoms = list(mol_template.atoms)
        a1, a2 = bond_idx
        # displace atom a2 along the bond vector
        vec = np.array(atoms[a2].xyz) - np.array(atoms[a1].xyz)
        vec = vec / np.linalg.norm(vec) * r
        atoms[a2] = Atom(atoms[a2].Z, (np.array(atoms[a1].xyz) + vec).tolist())
        mol_r = Molecule(atoms, mol_template.charge, mol_template.multiplicity)
        m = GFN2Model(mol_r, params=model.params, warn=False)
        result.append(m.energy())
    return np.array(result)

# OH bond scan in water
r_vals = np.linspace(1.2, 3.0, 19)
energies = scan_bond(mol, (0, 1), r_vals)
e_min = energies.min()
for r, e in zip(r_vals, energies):
    print(f"  r(OH) = {r:.2f} bohr  E = {e:.8f} Ha  ΔE = {(e - e_min)*627.509:.2f} kcal/mol")

Angle scan

def scan_angle(mol_template, angle_deg_vals):
    """Return GFN2-xTB energies for a bond-angle scan, keeping both OH bonds equal."""
    result = []
    for angle_deg in angle_deg_vals:
        theta = np.deg2rad(angle_deg / 2.0)
        r_oh = 1.81  # fixed bond length
        atoms = [
            Atom(8, [0.0, 0.0, 0.0]),
            Atom(1, [r_oh * np.sin(theta), r_oh * np.cos(theta), 0.0]),
            Atom(1, [-r_oh * np.sin(theta), r_oh * np.cos(theta), 0.0]),
        ]
        mol_a = Molecule(atoms)
        m = GFN2Model(mol_a, params=params, warn=False)
        result.append(m.energy())
    return np.array(result)

angles = np.linspace(70, 140, 15)
e_angles = scan_angle(mol, angles)
e_amin = e_angles.min()
print("\nAngle scan:")
for ang, e in zip(angles, e_angles):
    print(f"  ∠HOH = {ang:5.1f}°  E = {e:.8f} Ha  ΔE = {(e - e_amin)*627.509:.2f} kcal/mol")

8. Periodic calculations

GFN2-xTB supports Γ-point and k-point periodic calculations through the native C++ driver.

Γ-point: molecular crystal

from vibeqc._vibeqc_core import PeriodicSystem, Atom as PAtom
import numpy as np

# H2O in a 10-bohr cubic box (molecular crystal)
lattice = np.eye(3) * 10.0
atoms = [
    PAtom(8, [0.0, 0.0, 0.0]),
    PAtom(1, [1.43, 0.98, 0.0]),
    PAtom(1, [-1.43, 0.98, 0.0]),
]
system = PeriodicSystem(3, lattice, atoms, charge=0, multiplicity=1)

opts = _xtb.XTBSccOptions()
opts.max_iter = 400
result = _xtb.run_gfn2_xtb_gamma(system, params, opts)
print(f"Periodic GFN2-xTB: E = {result.energy:.8f} Ha")
print(f"  free energy A = {result.free_energy:.8f} Ha")
print(f"  n_cells={result.n_cells}  n_iter={result.n_iter}  converged={result.converged}")

Periodic GFN2-xTB smears the frontier by default (0.001 Ha, about 316 K), so result.smearing_temperature, result.entropy, and the Mermin free energy result.free_energy = energy - T*S are populated on every periodic run. Pass opts.electronic_temperature = 0.0 explicitly for the exact zero-temperature Aufbau occupations.

Graphene: 2D with Γ-point

# Graphene 2-atom unit cell (a = 2.46 Å ≈ 4.65 bohr)
a = 4.65
c = 20.0
lattice_gr = np.array([
    [a, 0, 0],
    [a / 2, a * np.sqrt(3) / 2, 0],
    [0, 0, c],
])
atoms_gr = [
    PAtom(6, [0.0, 0.0, 0.0]),
    PAtom(6, [a / 2, a * np.sqrt(3) / 6, 0.0]),
]
system_gr = PeriodicSystem(2, lattice_gr, atoms_gr, charge=0, multiplicity=1)

opts.max_iter = 400
result_gr = _xtb.run_gfn2_xtb_gamma(system_gr, params, opts)
print(f"Graphene GFN2-xTB: E = {result_gr.energy:.8f} Ha/cell")
print(f"  n_cells={result_gr.n_cells}  converged={result_gr.converged}")

k-point sampling

For accurate band energies in periodic systems, sample the Brillouin zone with a Monkhorst-Pack mesh:

from vibeqc._vibeqc_core import monkhorst_pack

kmesh = monkhorst_pack(system_gr, (4, 4, 1))
result_k = _xtb.run_gfn2_xtb_kpoints(system_gr, params, kmesh, opts)
print(f"Graphene (4×4×1 k-points): E = {result_k.energy:.8f} Ha/cell")
print(f"  n_kpoints={result_k.n_kpoints}  converged={result_k.converged}")

# Band energies at each k-point (flat list: n_kpoints * n_bands floats)
n_bands = len(result_k.band_energies) // result_k.n_kpoints
for ik in range(min(result_k.n_kpoints, 4)):
    start = ik * n_bands
    bands = result_k.band_energies[start:start + n_bands]
    print(f"  k-point {ik}: bands[:5] = {[f'{b:.3f}' for b in bands[:5]]} Ha")

Periodic gradients and stress

from vibeqc._vibeqc_core import semiempirical as _se

# Gradient (fixed-charge approximation)
grad = _se.compute_periodic_gfn2_gradient(system_gr, result_gr, params)
print(f"Gradient shape: {grad.shape}")
print(f"|grad|_max:     {np.abs(grad).max():.6f} Ha/bohr")

# Stress tensor
stress = _se.compute_periodic_gfn2_stress(system_gr, result_gr, params)
print(f"Stress (Ha/bohr³):\n{np.array2string(stress, precision=6)}")

9. Convergence tuning

The default GFN2-xTB SCC uses a simple damping mixer with charge mixing 0.1 and up to 3600 iterations. For difficult cases, tune the mixer:

opts = _xtb.XTBSccOptions()

# Simple mixer -- robust, slow
opts.scc_mixer = _se.SCCMixer.Simple
opts.charge_mixing = 0.15
opts.max_iter = 1000

# DIIS -- faster for well-behaved systems
opts.scc_mixer = _se.SCCMixer.DIIS
opts.mixer_memory = 8       # DIIS subspace size
opts.mixer_damping = 0.3

# Broyden -- best for periodic / polar / transition metals
opts.scc_mixer = _se.SCCMixer.Broyden
opts.mixer_memory = 12      # Broyden history length
opts.mixer_damping = 0.2
opts.max_iter = 2000

# Electronic temperature (molecular: default 0 = exact Aufbau; periodic
# GFN2-xTB: default 0.001 Ha frontier smearing, explicit 0 restores Aufbau)
opts.electronic_temperature = 0.001  # Ha (~315 K)

# Automatic stabilization -- when the primary solve fails, retry with
# a more conservative recipe
opts.auto_stabilize = True

result = _xtb.run_gfn2_xtb(mol, params, opts)
print(f"converged={result.converged}  n_iter={result.n_iter}")

# Identical options are forwarded through GFN2Model:
model = GFN2Model(
    mol, params=params, warn=False,
    scc_mixer="broyden",
    charge_mixing=0.2,
    mixer_memory=12,
    mixer_damping=0.1,
    max_iter=500,
)
print(f"energy={model.energy():.8f}  n_iter={model.n_iter}")

Mixer selection guide

Mixer

scc_mixer=

Best for

Simple damping

"simple"

The default: a two-phase polyalgorithm (damped simple mixing with stall-adaptive step halving, then a guarded DIIS handoff)

DIIS

"diis"

Explicit unguarded accelerator (bypasses the polyalgorithm)

Broyden

"broyden"

Explicit unguarded accelerator (bypasses the polyalgorithm)

The AES faithfulness flag

opts.aes_faithful = True requests the full AES (anisotropic electrostatic) multipole expansion up to quadrupole, matching the reference xtb implementation exactly. When False, vibe-qc uses a dipole-only AES for speed; the default is False. Production-parity studies should use True.

Element coverage

GFN2-xTB is parametrised for elements up to radon (Z = 86). The post-SCF native D4 dispersion is parity-validated for H, He, B, C, N, O, F, and Ne. For elements outside that set, D4 returns zero and emits a GFN2D4UnsupportedWarning.

from vibeqc.semiempirical.methods.gfn2 import GFN2D4UnsupportedWarning

# Molecule with chlorine (Z=17, outside native D4 set)
import warnings
ccl4 = Molecule([
    Atom(6, [0, 0, 0]),
    Atom(17, [1.5, 1.5, 1.5]),
    Atom(17, [-1.5, -1.5, 1.5]),
    Atom(17, [1.5, -1.5, -1.5]),
    Atom(17, [-1.5, 1.5, -1.5]),
])
with warnings.catch_warnings(record=True) as w:
    warnings.simplefilter("always")
    model_cl = GFN2Model(ccl4, params=params, warn=False)
    e = model_cl.energy()
    d4_warnings = [x for x in w if issubclass(x.category, GFN2D4UnsupportedWarning)]
    if d4_warnings:
        print(f"D4 unsupported: {d4_warnings[0].message}")
    print(f"CCl4 GFN2-xTB: {e:.8f} Ha (D4=0 for Cl)")

Summary – what vibe-qc’s GFN2-xTB can and cannot do

Workshop topic

vibe-qc GFN2-xTB

Notes

Single-point

✓

Molecular + periodic Γ-point + k-point

Spin polarization

✓

run_ugfn2_xtb

Geometry optimization

✓

Via run_job or ASE

Frequencies

✓

FD Hessian from analytic gradients

Molecular dynamics

✓

ASE NVE / NVT with SemiempiricalCalculator

PES scans

✓

Programmatic bond/angle scans

Periodic

✓

1D/2D/3D, Γ + k-points, gradient + stress

Solvation

✗

Not implemented

ONIOM

✗

Not implemented

Docking

✗

Not implemented

Thermo submodule

✗

Use ASE thermochemistry or vibeqc.thermo

GFN1-xTB

✗

Not implemented

GFN-FF

✗

Not implemented

See also