More semiempirical methods: PM6 and GFN2-xTB

Beyond DFTB and MSINDO, vibe-qc ships two more semiempirical engines for fast energies and gradients on large systems: PM6, a production NDDO method, and GFN2-xTB, an extended tight-binding method that is experimental in vibe-qc. Both are far cheaper than a minimal-basis Hartree-Fock and are the right tool for preoptimization, conformer screening, and systems too large for ab initio.

PM6

PM6 is selected with method="pm6" and needs no basis-set argument: it carries its own minimal valence basis and parameter set.

import vibeqc as vq

mol = vq.Molecule([vq.Atom(8, [0, 0, 0]),
                   vq.Atom(1, [0,  1.43, -0.98]),
                   vq.Atom(1, [0, -1.43, -0.98])])
result = vq.run_job(mol, method="pm6", output="h2o_pm6")
print(result.energy)

SCF converged in 8 iterations;  E = -10.21015659 Ha

PM6 provides energies and finite-difference gradients, so it plugs into geometry optimization the same way the ab-initio methods do.

GFN2-xTB (experimental)

GFN2-xTB is Grimme’s extended semiempirical tight-binding method. It is experimental in vibe-qc and emits a GFN2ExperimentalWarning, which you opt past explicitly:

import warnings
import vibeqc as vq

with warnings.catch_warnings():
    warnings.simplefilter("ignore")          # opt in to the experimental method
    result = vq.run_job(mol, method="gfn2_xtb", output="h2o_gfn2")
print(result.energy)

E = -4.85491960 Ha

Reading the numbers

Semiempirical total energies live on each method’s own parametrised reference scale. The PM6 number (−10.21 Ha) and the GFN2-xTB number (−4.85 Ha) are not comparable to each other, nor to an ab-initio total (the same water at HF / 6-31G* is −76.01 Ha). What is meaningful is relative energies within one method (conformers, reaction energies) and the geometries and properties the method produces. The standard workflow is to get a structure or a trend fast with a semiempirical method, then refine the energetics with DFT or a correlated method.

See also